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Skip to main content

404

Page not found

That’s a Four-Oh-Four.

Somewhere to recap knowledge
Copyright © 2025 Dale
- + diff --git a/article/index.html b/article/index.html index f9b936ae..cb029007 100644 --- a/article/index.html +++ b/article/index.html @@ -30,14 +30,14 @@ Articles | Know No Evil - +
Skip to main content
Research Topics

Here are some candidate research topics for second-year in UniMelb.

-
DaleUniMelb
Brain-inspired computing by assemblies of neurons

Although deep neural networks have been widely adopted and proven effective across many applications, the backpropagation process remains complex and not easily interpretable in a way that mirrors human understanding. Humans are capable of swiftly grasping and interpreting complex patterns to make decisions, whereas neural networks depend on computationally intensive methods for learning.

Dale25s1
Breath Tracking using a Depth Camera for XV Scanner

The XV Scanner™ is the world’s only fully dedicated X-ray lung function scanner, designed to do something completely unique–capture a single, full-breath cycle in motion, from four different angles simultaneously.

-

To optimise the scanning procedure, it is desirable to have a real-time, non-contact method for monitoring the patient’s breathing. This project is focused on the development of a depth-camera based breath tracking method/algorithm to extract a highly dependable, real-time breath trace from a seated patient. As the breath trace may be used for automatically initiating and terminating X-ray exposures during the patient’s breathing cycle.

Dale25s1
Images to Text for Museum Collection

Museum collections are an important source of information in understanding the historical knowledge on culture, people and scientific discoveries. Museums host range of data including metadata (including descriptive texts), and images of objects (from multiple views) and notes. For this project, we use the collections from the University of Melbourne Medical museums (Medical History Museum, Henry Forman Atkinson Dental Museum, Harry Brookes Allen Museum of Anatomy and Pathology).

Dale25s1
Cloud-Device Collaborative Learning via small and large lanauge models for mobile health

With the growing adoption of wearable and mobile devices, the use of AI in mobile health is becoming increasingly relevant for a variety of healthcare applications. However, deploying AI models on these resource-limited client devices poses significant challenges, restricting their applicability. This is especially the case with the introduction of large language models (LLMs) and multimodal LLMs, which require substantial memory and therefore present additional deployment hurdles.

Dale25s1
Model Reprogramming for Low-Level Image Processing

Model reprogramming [1] or input visual prompts [2] has been successfully used in reusing pretrained image classifiers. This project aims to explore whether model reprogramming can enable the transferability of pretrained deep neural networks across low-level image processing tasks. Specifically, we will investigate the feasibility of using pretrained models (e.g., U-Net) for image denoising, enhancement, and super-resolution on different tasks by introducing task-specific visual prompts.

Dale25s1
Efficient Cloth Try-on Image Synthesis via Denoising Diffusion Model

Pose-guided person image synthesis task aims to render a person’s image with a desired pose and appearance. Specifically, the appearance is defined by a given source image and the pose by a set of keypoints. Having control over the synthesized person images in terms of pose and style is an important requisite for applications such as ecommerce, virtual reality, metaverse and content generation for the entertainment industry. However, the current solutions usually require a significant amount of time to generate an image. This project aims to develop a new diffusion model that can generate images in seconds.

Dale25s1
DL/QML的学习资料

Resource on DL/AI (Maybe watch/listen the seminars/podcasts first, then the online courses and books)

+
DaleUniMelb
Brain-inspired computing by assemblies of neurons

Although deep neural networks have been widely adopted and proven effective across many applications, the backpropagation process remains complex and not easily interpretable in a way that mirrors human understanding. Humans are capable of swiftly grasping and interpreting complex patterns to make decisions, whereas neural networks depend on computationally intensive methods for learning.

Dale25S1
Breath Tracking using a Depth Camera for XV Scanner

The XV Scanner™ is the world’s only fully dedicated X-ray lung function scanner, designed to do something completely unique–capture a single, full-breath cycle in motion, from four different angles simultaneously.

+

To optimise the scanning procedure, it is desirable to have a real-time, non-contact method for monitoring the patient’s breathing. This project is focused on the development of a depth-camera based breath tracking method/algorithm to extract a highly dependable, real-time breath trace from a seated patient. As the breath trace may be used for automatically initiating and terminating X-ray exposures during the patient’s breathing cycle.

Dale25S1
Images to Text for Museum Collection

Museum collections are an important source of information in understanding the historical knowledge on culture, people and scientific discoveries. Museums host range of data including metadata (including descriptive texts), and images of objects (from multiple views) and notes. For this project, we use the collections from the University of Melbourne Medical museums (Medical History Museum, Henry Forman Atkinson Dental Museum, Harry Brookes Allen Museum of Anatomy and Pathology).

Dale25S1
Cloud-Device Collaborative Learning via small and large lanauge models for mobile health

With the growing adoption of wearable and mobile devices, the use of AI in mobile health is becoming increasingly relevant for a variety of healthcare applications. However, deploying AI models on these resource-limited client devices poses significant challenges, restricting their applicability. This is especially the case with the introduction of large language models (LLMs) and multimodal LLMs, which require substantial memory and therefore present additional deployment hurdles.

Dale25S1
Model Reprogramming for Low-Level Image Processing

Model reprogramming [1] or input visual prompts [2] has been successfully used in reusing pretrained image classifiers. This project aims to explore whether model reprogramming can enable the transferability of pretrained deep neural networks across low-level image processing tasks. Specifically, we will investigate the feasibility of using pretrained models (e.g., U-Net) for image denoising, enhancement, and super-resolution on different tasks by introducing task-specific visual prompts.

Dale25S1
Efficient Cloth Try-on Image Synthesis via Denoising Diffusion Model

Pose-guided person image synthesis task aims to render a person’s image with a desired pose and appearance. Specifically, the appearance is defined by a given source image and the pose by a set of keypoints. Having control over the synthesized person images in terms of pose and style is an important requisite for applications such as ecommerce, virtual reality, metaverse and content generation for the entertainment industry. However, the current solutions usually require a significant amount of time to generate an image. This project aims to develop a new diffusion model that can generate images in seconds.

Dale25S1
DL/QML的学习资料

Resource on DL/AI (Maybe watch/listen the seminars/podcasts first, then the online courses and books)

  • Books & Online Course Materials:

    @@ -206,6 +206,6 @@
  • The expression represents the expected value of observing on .
  • Observables in general do not commute. This means that the order of observation matters. Moreover, if the commutator of two observables is not zero, there is an intrinsic limit to our capability of measuring their values simultaneously.
DaleUniMelb24S2Quantum ComputingLinear AlgebraComplex Numbers
Somewhere to recap knowledge
Copyright © 2025 Dale
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404 Not Found

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Prerequisites 1. Introduction to Genomics 基因组学

1. Indexing

2. Sequence Alignment and Mapping

3.1. Comparing Sequences

3.2. Graph Theory

4.1. Network Analysis

4.2. Advanced Indexing

5. Evolutionary Trees

',15),o=i("

  • taxonomy 分类学

  • phylogenetics 系统发育

  • speciation 物种形成

  • multiple sequence alignment

  • greedy: pair the most similar sequences

  • building a tree

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  • distance based methods

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  • small parsimony

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  • overlap layout consensus (OLC)

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  • software

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    7. Dimensionality Reduction

    8. Clustering

    9. Optimisation

    10. Supervised Machine Learning

    11. Evaluation

    Guest Lecture: An introduction to machine learning and artificial intelligence

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    Prerequisites 1. Introduction to Genomics 基因组学

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    Prerequisites 1. Introduction to Genomics 基因组学

    1. Indexing

    2. Sequence Alignment and Mapping

    3.1. Comparing Sequences

    3.2. Graph Theory

    4.1. Network Analysis

    4.2. Advanced Indexing

    5. Evolutionary Trees

    ',15),o=i("

  • taxonomy 分类学

  • phylogenetics 系统发育

  • speciation 物种形成

  • multiple sequence alignment

  • greedy: pair the most similar sequences

  • building a tree

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  • distance based methods

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  • parsimony 解析 methods

  • evaluating trees

  • small parsimony

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  • overlap layout consensus (OLC)

  • sequencing by hybridisation

  • De Bruijn Graphs

  • considerations

  • OLC simplification

  • De Bruijn graph simplification

  • Kmers, coverage, depth

  • assessing assemblies

  • software

    • ",9),F=i('

    7. Dimensionality Reduction

    8. Clustering

    9. Optimisation

    10. Supervised Machine Learning

    11. Evaluation

    Guest Lecture: An introduction to machine learning and artificial intelligence

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    Prerequisites 1. Introduction to Genomics 基因组学

    \\n","autoDesc":true}`);export{X as comp,Y as data}; diff --git a/assets/COMP90018.html-CaPAv_JT.js b/assets/COMP90018.html-CrMJZzLo.js similarity index 99% rename from assets/COMP90018.html-CaPAv_JT.js rename to assets/COMP90018.html-CrMJZzLo.js index 18bbc5b8..55509130 100644 --- a/assets/COMP90018.html-CaPAv_JT.js +++ b/assets/COMP90018.html-CrMJZzLo.js @@ -1 +1 @@ -import{_ as l}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as a,b as e,d as n,a as t,o as i}from"./app-CF0Bed9T.js";const s={},o=t('

    Does any resource help with this course?

    Ubiquitous Computing Fundamentals

    What is this course about?

    This course is an introduction to mobile computing systems programming for engineering students.

    What is the ubiquitous computing?

    A vision of computing in which technology is embedded in the world around us, providing a seamless and unobtrusive experience to users as they go about their daily lives.

    What are the main aspects of UI development for mobile devices?

    What is the action cycle in UI development?

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    What are the steps involved in the execution phase of the action cycle?

    What is cross-platform design in UI development?

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    What is the relationship between technology and human perception?

    What is ubiquitous interaction?

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    What is Moore's Law?

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    What are some examples of mobile sensors?

    What is the activity lifecycle in the context of sensors?

    How is measurement represented in signal processing?

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    What is cloaking in location-based services?

    What is a decentralized system?

    What is negative information?

    What are some methods of social engineering?

    What are CAPTCHAs?

    What are some long-standing privacy/security issues?

    What are the characteristics of augmented reality?

    What technologies are used in augmented reality?

    What are the limitations of augmented reality?

    What is an interesting experiment in augmented reality?

    What is an advanced evaluation method?

    What topics were covered in the guest lecture on mobile sensing and edge intelligence?

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    Does any resource help with this course?

    \\n

    Ubiquitous Computing Fundamentals

    \\n

    What is this course about?

    ","autoDesc":true}`);export{R as comp,I as data}; +import{_ as l}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as a,b as e,d as n,a as t,o as i}from"./app-Cb1RROb0.js";const s={},o=t('

    Does any resource help with this course?

    Ubiquitous Computing Fundamentals

    What is this course about?

    This course is an introduction to mobile computing systems programming for engineering students.

    What is the ubiquitous computing?

    A vision of computing in which technology is embedded in the world around us, providing a seamless and unobtrusive experience to users as they go about their daily lives.

    What are the main aspects of UI development for mobile devices?

    What is the action cycle in UI development?

    What are the steps involved in the evaluation phase of the action cycle?

    What are the steps involved in the execution phase of the action cycle?

    What is cross-platform design in UI development?

    What are the key aspects of usability in UI development?

    What is the relationship between technology and human perception?

    What is ubiquitous interaction?

    What is the hardware renaissance?

    What is Moore's Law?

    How do sensors convert physical phenomena into data?

    What are some examples of mobile sensors?

    What is the activity lifecycle in the context of sensors?

    How is measurement represented in signal processing?

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    What are the characteristics of mean/median filters?

    What are the characteristics of a Kalman filter?

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    What is cloaking in location-based services?

    What is a decentralized system?

    What is negative information?

    What are some methods of social engineering?

    What are CAPTCHAs?

    What are some long-standing privacy/security issues?

    What are the characteristics of augmented reality?

    What technologies are used in augmented reality?

    What are the limitations of augmented reality?

    What is an interesting experiment in augmented reality?

    What is an advanced evaluation method?

    What topics were covered in the guest lecture on mobile sensing and edge intelligence?

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    Does any resource help with this course?

    \\n

    Ubiquitous Computing Fundamentals

    \\n

    What is this course about?

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    What is this course about?

    This course is an introduction to natural language processing for engineering students. The course covers the following topics:

    1. Preprocessing
    1. N-gram Language Model
    1. Text Classification
    1. Part of Speech Tagging
    1. Hidden Markov Model
    1. Feedforward Neural Network
    1. Recurrent Neural Network
    1. Lexical Semantics
    1. Distributional Semantics
    1. Contextual Representation
    1. Attention
    1. Machine Translation
    1. Transformer
    1. Pretrained Language Models
    1. Large Language Models
    1. Named Entity Recognition
    1. Coreference Resolution 共指消解
    1. Question Answering and Reading Comprehension
    1. Spoken Language Understanding
    1. Vision Language Pretrained Model
    1. Ethics

    What is Natural Language Processing?

    Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves the development of algorithms and models that enable computers to understand, interpret, and generate human language. NLP is used in a wide range of applications, including machine translation, sentiment analysis, chatbots, and speech recognition.

    ',46),o=[n];function s(r,u){return e(),l("div",null,o)}const d=i(a,[["render",s],["__file","COMP90042.html.vue"]]),m=JSON.parse('{"path":"/master/COMP90042.html","title":"COMP90042 Natural Language Processing","lang":"en-US","frontmatter":{"title":"COMP90042 Natural Language Processing","shortTitle":"COMP90042","order":1,"icon":"book-open","category":["UniMelb","24S1"],"tag":["Artificial Intelligence","Machine Learning","Deep Learning","Natural Language Processing"],"description":"What is this course about? This course is an introduction to natural language processing for engineering students. The course covers the following topics: Preprocessing sentence...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90042.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90042 Natural Language Processing"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to natural language processing for engineering students. The course covers the following topics: Preprocessing sentence..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Artificial Intelligence"}],["meta",{"property":"article:tag","content":"Machine Learning"}],["meta",{"property":"article:tag","content":"Deep Learning"}],["meta",{"property":"article:tag","content":"Natural Language Processing"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"COMP90042 Natural Language Processing\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is Natural Language Processing?","slug":"what-is-natural-language-processing","link":"#what-is-natural-language-processing","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":4},{"name":"shyu216","email":"yusihong073@gmail.com","commits":2}]},"filePathRelative":"master/COMP90042.md","localizedDate":"August 1, 2024","excerpt":"

    What is this course about?

    \\n

    This course is an introduction to natural language processing for engineering students. The course covers the following topics:

    \\n
      \\n
    1. Preprocessing
      2. \\n
    \\n","autoDesc":true}');export{d as comp,m as data}; +import{_ as i}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as l,o as e,a as t}from"./app-Cb1RROb0.js";const a={},n=t('

    What is this course about?

    This course is an introduction to natural language processing for engineering students. The course covers the following topics:

    1. Preprocessing
    1. N-gram Language Model
    1. Text Classification
    1. Part of Speech Tagging
    1. Hidden Markov Model
    1. Feedforward Neural Network
    1. Recurrent Neural Network
    1. Lexical Semantics
    1. Distributional Semantics
    1. Contextual Representation
    1. Attention
    1. Machine Translation
    1. Transformer
    1. Pretrained Language Models
    1. Large Language Models
    1. Named Entity Recognition
    1. Coreference Resolution 共指消解
    1. Question Answering and Reading Comprehension
    1. Spoken Language Understanding
    1. Vision Language Pretrained Model
    1. Ethics

    What is Natural Language Processing?

    Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves the development of algorithms and models that enable computers to understand, interpret, and generate human language. NLP is used in a wide range of applications, including machine translation, sentiment analysis, chatbots, and speech recognition.

    ',46),o=[n];function s(r,u){return e(),l("div",null,o)}const d=i(a,[["render",s],["__file","COMP90042.html.vue"]]),m=JSON.parse('{"path":"/master/COMP90042.html","title":"COMP90042 Natural Language Processing","lang":"en-US","frontmatter":{"title":"COMP90042 Natural Language Processing","shortTitle":"COMP90042","order":1,"icon":"book-open","category":["UniMelb","24S1"],"tag":["Artificial Intelligence","Machine Learning","Deep Learning","Natural Language Processing"],"description":"What is this course about? This course is an introduction to natural language processing for engineering students. The course covers the following topics: Preprocessing sentence...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90042.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90042 Natural Language Processing"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to natural language processing for engineering students. The course covers the following topics: Preprocessing sentence..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Artificial Intelligence"}],["meta",{"property":"article:tag","content":"Machine Learning"}],["meta",{"property":"article:tag","content":"Deep Learning"}],["meta",{"property":"article:tag","content":"Natural Language Processing"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"COMP90042 Natural Language Processing\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is Natural Language Processing?","slug":"what-is-natural-language-processing","link":"#what-is-natural-language-processing","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":4},{"name":"shyu216","email":"yusihong073@gmail.com","commits":2}]},"filePathRelative":"master/COMP90042.md","localizedDate":"August 1, 2024","excerpt":"

    What is this course about?

    \\n

    This course is an introduction to natural language processing for engineering students. The course covers the following topics:

    \\n
      \\n
    1. Preprocessing
      2. \\n
    \\n","autoDesc":true}');export{d as comp,m as data}; diff --git a/assets/COMP90044.html-BbEhbKqv.js b/assets/COMP90044.html-CA-SqVVE.js similarity index 99% rename from assets/COMP90044.html-BbEhbKqv.js rename to assets/COMP90044.html-CA-SqVVE.js index b67e808e..0514eb0f 100644 --- a/assets/COMP90044.html-BbEhbKqv.js +++ b/assets/COMP90044.html-CA-SqVVE.js @@ -1 +1 @@ -import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as a,o as t,a as i}from"./app-CF0Bed9T.js";const s={},r=i('

    What is this course about?

    This course is an introduction to research methods for computer science students.

    What is research?

    In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.

    What is a good research question?

    A good research question should be clear, focused, researchable, and sufficiently complex.

    How to formulate a research question?

    1. Identify a broader subject
    2. Research existing literature and data
    3. Use the three-step formula:

    What is the purpose of a literature review?

    The purpose of a literature review is to provide understanding, identify gaps, establish a theoretical framework, and highlight significance.

    What are the expectations for research writing?

    What should not be included in research writing?

    Use advanced search queries like (Robotics OR “Artificial Intelligence”) AND Vision.

    How to read and write critically?

    Read and write with questions in mind, focusing on objectives, motivations, basis, and significance.

    Don't say "it is popular" or "it is important". Always ask "why".

    What types of research are there?

    What are the suggestions for publishing?

    What are common mistakes in performance analysis?

    1. Is the system correctly defined and the goals clearly stated?
    2. Are the goals stated in an unbiased manner?
    3. Have all the steps of the analysis followed systematically?
    4. Is the problem clearly understood before analyzing it?
    5. Are the performance metrics relevant for this problem?
    6. Is the workload correct for this problem?
    7. Is the evaluation technique appropriate?
    8. Is the list of parameters that affect performance complete?
    9. Have all parameters that affect performance been chosen as factors to be varied?
    10. Is the experimental design efficient in terms of time and results?
    11. Is the level of detail proper?
    12. Is the measured data presented with analysis and interpretation?
    13. Is the analysis statistically correct?
    14. Has the sensitivity analysis been done?
    15. Would errors in the input cause an insignificant change in the results?
    16. Have the outliers in the input or output been treated properly?
    17. Have the future changes in the system and workload been modeled?
    18. Has the variance of input been taken into account?
    19. Has the variance of the result been analyzed?
    20. Is the analysis easy to explain?
    21. Is the presentation style suitable for its audience?
    22. Have the results been presented graphically as much as possible?
    23. Are the assumptions and limitations of the analysis clearly documented?

    How should the opening of a presentation be?

    The opening is the only chance to grasp attention.

    How to control the number of slides in a presentation?

    1 or 2 minutes per slide, go fast because the audience forgets fast.

    How should the conclusion of a presentation be?

    The conclusion should leave a long-lasting impression.

    What is the role of statistics in research?

    Statistics are used to prove hypotheses, such as using a t-test to determine if there is a significant difference between the means of two groups.

    What are the ethical issues in research?

    What are research metrics?

    Research metrics are systems or standards of measurement, including publications, citations, and impact factors.

    How to assemble a good research argument?

    How to clarify research aims and objectives?

    What are milestones and deliverables in research planning?

    What are the risks in research?

    What are the methods of research planning?

    What is qualitative research?

    Qualitative research is a method of inquiry that focuses on understanding the meaning of social phenomena with the goal of developing theories.

    ',49),n=[r];function o(h,l){return t(),a("div",null,n)}const u=e(s,[["render",o],["__file","COMP90044.html.vue"]]),p=JSON.parse('{"path":"/master/COMP90044.html","title":"COMP90044 Research Methods","lang":"en-US","frontmatter":{"title":"COMP90044 Research Methods","shortTitle":"COMP90044","order":2,"icon":"book-open","category":["UniMelb","24S2"],"tag":["Research"],"description":"What is this course about? This course is an introduction to research methods for computer science students. What is research? In a research domain, come up with a research ques...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90044.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90044 Research Methods"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to research methods for computer science students. What is research? 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research?","slug":"what-is-research","link":"#what-is-research","children":[]},{"level":3,"title":"What is a good research question?","slug":"what-is-a-good-research-question","link":"#what-is-a-good-research-question","children":[]},{"level":3,"title":"How to formulate a research question?","slug":"how-to-formulate-a-research-question","link":"#how-to-formulate-a-research-question","children":[]},{"level":3,"title":"What is the purpose of a literature review?","slug":"what-is-the-purpose-of-a-literature-review","link":"#what-is-the-purpose-of-a-literature-review","children":[]},{"level":3,"title":"What are the expectations for research writing?","slug":"what-are-the-expectations-for-research-writing","link":"#what-are-the-expectations-for-research-writing","children":[]},{"level":3,"title":"What should not be included in research writing?","slug":"what-should-not-be-included-in-research-writing","link":"#what-should-not-be-included-in-research-writing","children":[]},{"level":3,"title":"How to conduct advanced search?","slug":"how-to-conduct-advanced-search","link":"#how-to-conduct-advanced-search","children":[]},{"level":3,"title":"How to read and write critically?","slug":"how-to-read-and-write-critically","link":"#how-to-read-and-write-critically","children":[]},{"level":3,"title":"What types of research are there?","slug":"what-types-of-research-are-there","link":"#what-types-of-research-are-there","children":[]},{"level":3,"title":"What are the suggestions for publishing?","slug":"what-are-the-suggestions-for-publishing","link":"#what-are-the-suggestions-for-publishing","children":[]},{"level":3,"title":"What are common mistakes in performance analysis?","slug":"what-are-common-mistakes-in-performance-analysis","link":"#what-are-common-mistakes-in-performance-analysis","children":[]},{"level":3,"title":"How should the opening of a presentation be?","slug":"how-should-the-opening-of-a-presentation-be","link":"#how-should-the-opening-of-a-presentation-be","children":[]},{"level":3,"title":"How to control the number of slides in a presentation?","slug":"how-to-control-the-number-of-slides-in-a-presentation","link":"#how-to-control-the-number-of-slides-in-a-presentation","children":[]},{"level":3,"title":"How should the conclusion of a presentation be?","slug":"how-should-the-conclusion-of-a-presentation-be","link":"#how-should-the-conclusion-of-a-presentation-be","children":[]},{"level":3,"title":"What is the role of statistics in research?","slug":"what-is-the-role-of-statistics-in-research","link":"#what-is-the-role-of-statistics-in-research","children":[]},{"level":3,"title":"What are the ethical issues in research?","slug":"what-are-the-ethical-issues-in-research","link":"#what-are-the-ethical-issues-in-research","children":[]},{"level":3,"title":"What are research metrics?","slug":"what-are-research-metrics","link":"#what-are-research-metrics","children":[]},{"level":3,"title":"How to assemble a good research argument?","slug":"how-to-assemble-a-good-research-argument","link":"#how-to-assemble-a-good-research-argument","children":[]},{"level":3,"title":"How to clarify research aims and objectives?","slug":"how-to-clarify-research-aims-and-objectives","link":"#how-to-clarify-research-aims-and-objectives","children":[]},{"level":3,"title":"What are milestones and deliverables in research planning?","slug":"what-are-milestones-and-deliverables-in-research-planning","link":"#what-are-milestones-and-deliverables-in-research-planning","children":[]},{"level":3,"title":"What are the risks in research?","slug":"what-are-the-risks-in-research","link":"#what-are-the-risks-in-research","children":[]},{"level":3,"title":"What are the methods of research planning?","slug":"what-are-the-methods-of-research-planning","link":"#what-are-the-methods-of-research-planning","children":[]},{"level":3,"title":"What is qualitative research?","slug":"what-is-qualitative-research","link":"#what-is-qualitative-research","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728648910000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":15},{"name":"shyu216","email":"yusihong073@gmail.com","commits":1}]},"filePathRelative":"master/COMP90044.md","localizedDate":"August 1, 2024","excerpt":"

    What is this course about?

    \\n

    This course is an introduction to research methods for computer science students.

    \\n

    What is research?

    \\n

    In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.

    ","autoDesc":true}');export{u as comp,p as data}; +import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as a,o as t,a as i}from"./app-Cb1RROb0.js";const s={},r=i('

    What is this course about?

    This course is an introduction to research methods for computer science students.

    What is research?

    In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.

    What is a good research question?

    A good research question should be clear, focused, researchable, and sufficiently complex.

    How to formulate a research question?

    1. Identify a broader subject
    2. Research existing literature and data
    3. Use the three-step formula:

    What is the purpose of a literature review?

    The purpose of a literature review is to provide understanding, identify gaps, establish a theoretical framework, and highlight significance.

    What are the expectations for research writing?

    What should not be included in research writing?

    Use advanced search queries like (Robotics OR “Artificial Intelligence”) AND Vision.

    How to read and write critically?

    Read and write with questions in mind, focusing on objectives, motivations, basis, and significance.

    Don't say "it is popular" or "it is important". Always ask "why".

    What types of research are there?

    What are the suggestions for publishing?

    What are common mistakes in performance analysis?

    1. Is the system correctly defined and the goals clearly stated?
    2. Are the goals stated in an unbiased manner?
    3. Have all the steps of the analysis followed systematically?
    4. Is the problem clearly understood before analyzing it?
    5. Are the performance metrics relevant for this problem?
    6. Is the workload correct for this problem?
    7. Is the evaluation technique appropriate?
    8. Is the list of parameters that affect performance complete?
    9. Have all parameters that affect performance been chosen as factors to be varied?
    10. Is the experimental design efficient in terms of time and results?
    11. Is the level of detail proper?
    12. Is the measured data presented with analysis and interpretation?
    13. Is the analysis statistically correct?
    14. Has the sensitivity analysis been done?
    15. Would errors in the input cause an insignificant change in the results?
    16. Have the outliers in the input or output been treated properly?
    17. Have the future changes in the system and workload been modeled?
    18. Has the variance of input been taken into account?
    19. Has the variance of the result been analyzed?
    20. Is the analysis easy to explain?
    21. Is the presentation style suitable for its audience?
    22. Have the results been presented graphically as much as possible?
    23. Are the assumptions and limitations of the analysis clearly documented?

    How should the opening of a presentation be?

    The opening is the only chance to grasp attention.

    How to control the number of slides in a presentation?

    1 or 2 minutes per slide, go fast because the audience forgets fast.

    How should the conclusion of a presentation be?

    The conclusion should leave a long-lasting impression.

    What is the role of statistics in research?

    Statistics are used to prove hypotheses, such as using a t-test to determine if there is a significant difference between the means of two groups.

    What are the ethical issues in research?

    What are research metrics?

    Research metrics are systems or standards of measurement, including publications, citations, and impact factors.

    How to assemble a good research argument?

    How to clarify research aims and objectives?

    What are milestones and deliverables in research planning?

    What are the risks in research?

    What are the methods of research planning?

    What is qualitative research?

    Qualitative research is a method of inquiry that focuses on understanding the meaning of social phenomena with the goal of developing theories.

    ',49),n=[r];function o(h,l){return t(),a("div",null,n)}const u=e(s,[["render",o],["__file","COMP90044.html.vue"]]),p=JSON.parse('{"path":"/master/COMP90044.html","title":"COMP90044 Research Methods","lang":"en-US","frontmatter":{"title":"COMP90044 Research Methods","shortTitle":"COMP90044","order":2,"icon":"book-open","category":["UniMelb","24S2"],"tag":["Research"],"description":"What is this course about? This course is an introduction to research methods for computer science students. What is research? In a research domain, come up with a research ques...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90044.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90044 Research Methods"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to research methods for computer science students. What is research? 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research?","slug":"what-is-research","link":"#what-is-research","children":[]},{"level":3,"title":"What is a good research question?","slug":"what-is-a-good-research-question","link":"#what-is-a-good-research-question","children":[]},{"level":3,"title":"How to formulate a research question?","slug":"how-to-formulate-a-research-question","link":"#how-to-formulate-a-research-question","children":[]},{"level":3,"title":"What is the purpose of a literature review?","slug":"what-is-the-purpose-of-a-literature-review","link":"#what-is-the-purpose-of-a-literature-review","children":[]},{"level":3,"title":"What are the expectations for research writing?","slug":"what-are-the-expectations-for-research-writing","link":"#what-are-the-expectations-for-research-writing","children":[]},{"level":3,"title":"What should not be included in research writing?","slug":"what-should-not-be-included-in-research-writing","link":"#what-should-not-be-included-in-research-writing","children":[]},{"level":3,"title":"How to conduct advanced search?","slug":"how-to-conduct-advanced-search","link":"#how-to-conduct-advanced-search","children":[]},{"level":3,"title":"How to read and write critically?","slug":"how-to-read-and-write-critically","link":"#how-to-read-and-write-critically","children":[]},{"level":3,"title":"What types of research are there?","slug":"what-types-of-research-are-there","link":"#what-types-of-research-are-there","children":[]},{"level":3,"title":"What are the suggestions for publishing?","slug":"what-are-the-suggestions-for-publishing","link":"#what-are-the-suggestions-for-publishing","children":[]},{"level":3,"title":"What are common mistakes in performance analysis?","slug":"what-are-common-mistakes-in-performance-analysis","link":"#what-are-common-mistakes-in-performance-analysis","children":[]},{"level":3,"title":"How should the opening of a presentation be?","slug":"how-should-the-opening-of-a-presentation-be","link":"#how-should-the-opening-of-a-presentation-be","children":[]},{"level":3,"title":"How to control the number of slides in a presentation?","slug":"how-to-control-the-number-of-slides-in-a-presentation","link":"#how-to-control-the-number-of-slides-in-a-presentation","children":[]},{"level":3,"title":"How should the conclusion of a presentation be?","slug":"how-should-the-conclusion-of-a-presentation-be","link":"#how-should-the-conclusion-of-a-presentation-be","children":[]},{"level":3,"title":"What is the role of statistics in research?","slug":"what-is-the-role-of-statistics-in-research","link":"#what-is-the-role-of-statistics-in-research","children":[]},{"level":3,"title":"What are the ethical issues in research?","slug":"what-are-the-ethical-issues-in-research","link":"#what-are-the-ethical-issues-in-research","children":[]},{"level":3,"title":"What are research metrics?","slug":"what-are-research-metrics","link":"#what-are-research-metrics","children":[]},{"level":3,"title":"How to assemble a good research argument?","slug":"how-to-assemble-a-good-research-argument","link":"#how-to-assemble-a-good-research-argument","children":[]},{"level":3,"title":"How to clarify research aims and objectives?","slug":"how-to-clarify-research-aims-and-objectives","link":"#how-to-clarify-research-aims-and-objectives","children":[]},{"level":3,"title":"What are milestones and deliverables in research planning?","slug":"what-are-milestones-and-deliverables-in-research-planning","link":"#what-are-milestones-and-deliverables-in-research-planning","children":[]},{"level":3,"title":"What are the risks in research?","slug":"what-are-the-risks-in-research","link":"#what-are-the-risks-in-research","children":[]},{"level":3,"title":"What are the methods of research planning?","slug":"what-are-the-methods-of-research-planning","link":"#what-are-the-methods-of-research-planning","children":[]},{"level":3,"title":"What is qualitative research?","slug":"what-is-qualitative-research","link":"#what-is-qualitative-research","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728648910000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":15},{"name":"shyu216","email":"yusihong073@gmail.com","commits":1}]},"filePathRelative":"master/COMP90044.md","localizedDate":"August 1, 2024","excerpt":"

    What is this course about?

    \\n

    This course is an introduction to research methods for computer science students.

    \\n

    What is research?

    \\n

    In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.

    ","autoDesc":true}');export{u as comp,p as data}; diff --git a/assets/COMP90054.html-DM9mMMXk.js b/assets/COMP90054.html-5HcvnLwb.js similarity index 99% rename from assets/COMP90054.html-DM9mMMXk.js rename to assets/COMP90054.html-5HcvnLwb.js index 29eef3f9..4fc48b5c 100644 --- a/assets/COMP90054.html-DM9mMMXk.js +++ b/assets/COMP90054.html-5HcvnLwb.js @@ -1,4 +1,4 @@ -import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,b as Q,d as a,a as s,o as T}from"./app-CF0Bed9T.js";const l={},n=s(`

    Does any resource help with this course?

    What is this course about?

    This course is an introduction to AI planning for autonomy. The course covers AI planning, including search algorithms, Markov decision processes, relaxation, and reinforcement learning. In the end, the course get in touch with game theory.

    What is AI Planning?

    AI planning is the process of determining a sequence of actions that will achieve a goal. It is a subfield of artificial intelligence that deals with the problem of finding a sequence of actions that will achieve a goal. AI planning is used in a wide range of applications, including robotics, logistics, and scheduling.

    Are there any applications of AI Planning?

    AI planning has a wide range of applications, including:

    What is BFS?

    队列，先进先出，后进后出。

    Optimal when costs are uniform.

    def breadthFirstSearch(problem):
+import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,b as Q,d as a,a as s,o as T}from"./app-Cb1RROb0.js";const l={},n=s(`
    Does any resource help with this course?
    What is this course about?
    This course is an introduction to AI planning for autonomy. The course covers AI planning, including search algorithms, Markov decision processes, relaxation, and reinforcement learning. In the end, the course get in touch with game theory.
    What is AI Planning?
    AI planning is the process of determining a sequence of actions that will achieve a goal. It is a subfield of artificial intelligence that deals with the problem of finding a sequence of actions that will achieve a goal. AI planning is used in a wide range of applications, including robotics, logistics, and scheduling.
    Are there any applications of AI Planning?
    AI planning has a wide range of applications, including:
    • Robotics: AI planning is used to plan the actions of robots to achieve a goal.
    • Game playing: AI planning is used to plan the actions of players in games.
    • Autonomous vehicles: AI planning is used to plan the actions of autonomous vehicles.
    What is BFS?
    队列，先进先出，后进后出。
    Optimal when costs are uniform.
    def breadthFirstSearch(problem):
     candidate_queue = util.Queue()
     init_state = problem.getStartState()
     init_path = []
diff --git a/assets/COMP90077.html-BTn3mtzf.js b/assets/COMP90077.html-3qbhTuUP.js
similarity index 98%
rename from assets/COMP90077.html-BTn3mtzf.js
rename to assets/COMP90077.html-3qbhTuUP.js
index e225ea56..7da5a801 100644
--- a/assets/COMP90077.html-BTn3mtzf.js
+++ b/assets/COMP90077.html-3qbhTuUP.js
@@ -1 +1 @@
-import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as i,a as r}from"./app-CF0Bed9T.js";const o={},a=r('
    Does any resource help with this course?
    1. Algorithm Design by Kleinberg and Tardos
    2. Algorithms by Jeff Erickson
    3. Linear Programming
    4. Design of Approximation Algorithms
    What is this course about?
    This course is an introduction to advanced algorithms and data structures for engineering students. The first part of the course covers advanced data structures, including treap, amortised analysis, quake heap, splay tree, hashing, range tree, and min cut/max flow. The second part of the course covers advanced algorithms, including linear programming, vertex cover, set cover, factor-x approximation, and duality.
    The course covers the following topics:
    1. Treap
    • tree + heap
    1. Amortised Analysis
    • always have balance
    • prepaid
    • potential
    1. Quake Heap
    • x tournament trees
    1. Splay Tree
    • splay after every operation
    • practical somehow
    1. Hashing
    • usually static/fixed size
    • perfect hashing: two-level hashing ensuring no collision
    • cuckoo hashing: x hash functions, allowing insertion
    1. Range Tree
    • multidimensional sort
    1. Min Cut/Max Flow
    • adjacency list
    • union find
    • matching
    • bipartite graph
    1. Linear Programming
    • given a set of linear inequalities, find the maximum/minimum value of a linear function
    • vertex cover
    • set cover
    • factor-x approximation
    • duality
    ',21),l=[a];function n(s,h){return i(),t("div",null,l)}const u=e(o,[["render",n],["__file","COMP90077.html.vue"]]),m=JSON.parse('{"path":"/master/COMP90077.html","title":"COMP90077 Advanced Algorithms and Data Structures","lang":"en-US","frontmatter":{"title":"COMP90077 Advanced Algorithms and Data Structures","shortTitle":"COMP90077","order":1,"icon":"book-open","category":["UniMelb","24S1"],"tag":["Data Structures","Algorithm","Graph Theory"],"description":"Does any resource help with this course? Algorithm Design by Kleinberg and Tardos Algorithms by Jeff Erickson Linear Programming Design of Approximation Algorithms What is this ...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90077.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90077 Advanced Algorithms and Data Structures"}],["meta",{"property":"og:description","content":"Does any resource help with this course? Algorithm Design by Kleinberg and Tardos Algorithms by Jeff Erickson Linear Programming Design of Approximation Algorithms What is this ..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Data Structures"}],["meta",{"property":"article:tag","content":"Algorithm"}],["meta",{"property":"article:tag","content":"Graph Theory"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"COMP90077 Advanced Algorithms and Data Structures\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"Does any resource help with this course?","slug":"does-any-resource-help-with-this-course","link":"#does-any-resource-help-with-this-course","children":[]},{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":4},{"name":"shyu216","email":"yusihong073@gmail.com","commits":2}]},"filePathRelative":"master/COMP90077.md","localizedDate":"August 1, 2024","excerpt":"
    Does any resource help with this course?
    \\n
      \\n
    1. Algorithm Design by Kleinberg and Tardos
      2. \\n
    2. Algorithms by Jeff Erickson
      3. \\n
    3. Linear Programming
      4. \\n
    4. Design of Approximation Algorithms
      5. \\n
    ","autoDesc":true}');export{u as comp,m as data};
+import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as i,a as r}from"./app-Cb1RROb0.js";const o={},a=r('
    Does any resource help with this course?
    1. Algorithm Design by Kleinberg and Tardos
    2. Algorithms by Jeff Erickson
    3. Linear Programming
    4. Design of Approximation Algorithms
    What is this course about?
    This course is an introduction to advanced algorithms and data structures for engineering students. The first part of the course covers advanced data structures, including treap, amortised analysis, quake heap, splay tree, hashing, range tree, and min cut/max flow. The second part of the course covers advanced algorithms, including linear programming, vertex cover, set cover, factor-x approximation, and duality.
    The course covers the following topics:
    1. Treap
    • tree + heap
    1. Amortised Analysis
    • always have balance
    • prepaid
    • potential
    1. Quake Heap
    • x tournament trees
    1. Splay Tree
    • splay after every operation
    • practical somehow
    1. Hashing
    • usually static/fixed size
    • perfect hashing: two-level hashing ensuring no collision
    • cuckoo hashing: x hash functions, allowing insertion
    1. Range Tree
    • multidimensional sort
    1. Min Cut/Max Flow
    • adjacency list
    • union find
    • matching
    • bipartite graph
    1. Linear Programming
    • given a set of linear inequalities, find the maximum/minimum value of a linear function
    • vertex cover
    • set cover
    • factor-x approximation
    • duality
    ',21),l=[a];function n(s,h){return i(),t("div",null,l)}const u=e(o,[["render",n],["__file","COMP90077.html.vue"]]),m=JSON.parse('{"path":"/master/COMP90077.html","title":"COMP90077 Advanced Algorithms and Data Structures","lang":"en-US","frontmatter":{"title":"COMP90077 Advanced Algorithms and Data Structures","shortTitle":"COMP90077","order":1,"icon":"book-open","category":["UniMelb","24S1"],"tag":["Data Structures","Algorithm","Graph Theory"],"description":"Does any resource help with this course? Algorithm Design by Kleinberg and Tardos Algorithms by Jeff Erickson Linear Programming Design of Approximation Algorithms What is this ...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/COMP90077.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"COMP90077 Advanced Algorithms and Data Structures"}],["meta",{"property":"og:description","content":"Does any resource help with this course? Algorithm Design by Kleinberg and Tardos Algorithms by Jeff Erickson Linear Programming Design of Approximation Algorithms What is this ..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Data Structures"}],["meta",{"property":"article:tag","content":"Algorithm"}],["meta",{"property":"article:tag","content":"Graph Theory"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"COMP90077 Advanced Algorithms and Data Structures\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"Does any resource help with this course?","slug":"does-any-resource-help-with-this-course","link":"#does-any-resource-help-with-this-course","children":[]},{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":4},{"name":"shyu216","email":"yusihong073@gmail.com","commits":2}]},"filePathRelative":"master/COMP90077.md","localizedDate":"August 1, 2024","excerpt":"
    Does any resource help with this course?
    \\n
      \\n
    1. Algorithm Design by Kleinberg and Tardos
      2. \\n
    2. Algorithms by Jeff Erickson
      3. \\n
    3. Linear Programming
      4. \\n
    4. Design of Approximation Algorithms
      5. \\n
    ","autoDesc":true}');export{u as comp,m as data};
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similarity index 99%
rename from assets/COMP90084.html-OSmUPhgM.js
rename to assets/COMP90084.html-DqnN4xPr.js
index 9d72b854..9acbd4cb 100644
--- a/assets/COMP90084.html-OSmUPhgM.js
+++ b/assets/COMP90084.html-DqnN4xPr.js
@@ -1 +1 @@
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    1. Linear Algebra to Quantum Computing
    • 为什么学？ 
      • 解决经典计算机无法解决的问题
      • 对于量子纠错可能性的乐观主义
    • 这是什么？ 
      • 基于量子力学的计算机学说
      • 量子化学，量子深度学习
      • 后量子密码学
    • Physical Qubits: 
      • Particles with polarization(photon) 光子
      • Trapped ions 离子阱
      • Cold Atoms 冷原子
      • Quantum dots 量子点
      • Using Defect in Crystals 晶体缺陷
      • Superconductors 超导体
    • Major Players 
      • IBM, Google, Microsoft
      • D-Wave, uses Quantum annealing to solve optimization problems
    • Model of Quantum Computer 
      • Input
      • Quantum Processor on Cloud 
        • Prepare a state
        • Apply gates
        • Measure
      • Output: the probability distribution of the measurement
    2. Classical to Quantum Systems and Basic Quantum Theory
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  • spin 自旋
    • prototypical way to implement quantum bits of information, or qubits
    • clockwise/anticlockwise
  • transition amplitude 转移振幅
    • inner product
    • determine how likely a start state will change to an end state (after measurement)
  • entanglement 纠缠
    • connect two qubits using a gate
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  • max-cut 
    • edges have weights
    • find a cut that maximizes the sum of the weights of the edges that are cut
  • minimum vetec cover 
    • find the minimum number of vertices that cover all edges
  • simulated annealing 模拟退火 
    • a probabilistic technique for approximating the global optimum of a given function
  • local search (Monte Carlo) 
    • find solution from neighbors
    • ",4),gn=Q("li",null,"reproduce annealing process",-1),cn={class:"MathJax",jax:"SVG",style:{position:"relative"}},Hn={style:{"vertical-align":"-0.357ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.409ex",height:"1.895ex",role:"img",focusable:"false",viewBox:"0 -680 1065 837.8","aria-hidden":"true"},pn=T('',1),un=[pn],fn=Q("mjx-assistive-mml",{unselectable:"on",display:"inline"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[Q("msub",null,[Q("mi",null,"E"),Q("mi",null,"i")])])],-1),Mn={class:"MathJax",jax:"SVG",style:{position:"relative"}},Ln={style:{"vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"0.781ex",height:"1.52ex",role:"img",focusable:"false",viewBox:"0 -661 345 672","aria-hidden":"true"},Vn=Q("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[Q("g",{"data-mml-node":"math"},[Q("g",{"data-mml-node":"mi"},[Q("path",{"data-c":"1D456",d:"M184 600Q184 624 203 642T247 661Q265 661 277 649T290 619Q290 596 270 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    6.1. Ising Hamiltonian and Quantum Adiabatic Annealing
    • physical system generates optimal solution 
      • map optimization problem to physical system ... cool the system to zero ... obtain the lowest energy state
      • an observable is represented by Hermitian matrix ... its eigenvalues are the measured values ... diagonalize the matrix and eigenvalues are the diagonal elements
    • Adiabatic theorem 
      • if a system is in the ground state of a Hamiltonian, and the Hamiltonian is slowly changed, then the system will remain in the ground state
    6.2. QAOA
    • quantum approximate optimization algorithm 量子近似优化算法
    ',4),ie={class:"hint-container tip"},ce=T('
    Tips
    A gpt's explaination of QAOA:
    量子近似优化算法（Quantum Approximate Optimization Algorithm，QAOA）是一种混合量子经典算法，用于解决组合优化问题。QAOA 结合了量子计算的并行性和经典优化的灵活性，旨在找到近似最优解。以下是 QAOA 的高层次解释：
    QAOA 的基本概念
    QAOA 通过构造一个参数化的量子电路来近似求解组合优化问题。该算法的核心思想是通过调整参数，使得量子电路生成的量子态在测量后能够提供优化问题的近似最优解。
    QAOA 的步骤
    ',6),He=Q("p",null,[Q("strong",null,"问题表示"),m("：")],-1),pe=Q("li",null,"将组合优化问题表示为一个二次无约束二进制优化（QUBO）问题或等价的 Ising 模型。",-1),ue={class:"MathJax",jax:"SVG",style:{position:"relative"}},fe={style:{"vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.373ex",height:"1.005ex",role:"img",focusable:"false",viewBox:"0 -444 607 444","aria-hidden":"true"},Me=T('',1),Le=[Me],Ve=Q("mjx-assistive-mml",{unselectable:"on",display:"inline"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mi",{mathvariant:"bold"},"x")])])],-1),Ze={class:"MathJax",jax:"SVG",style:{position:"relative"}},xe={style:{"vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.853ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 2145 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    QAOA 的优点
    • 并行性：利用量子计算的并行性，可以同时探索多个解空间。
    • 灵活性：通过调整参数，可以适应不同的优化问题。
    • 近似最优解：即使在量子计算资源有限的情况下，QAOA 也能找到高质量的近似解。
    QAOA 的应用
    QAOA 可以应用于各种组合优化问题，包括但不限于：
    • 最大割问题（Max-Cut）
    • 旅行商问题（TSP）
    • 图着色问题
    • 集合覆盖问题
    • 资源分配问题
    ',5),gs=T('
    7.1. Quantum Key Distribution
    • Alice and Bob agree in advance a set with two different basis, say the z-basis or the x-basis
    • Alice sends encoded qubits to Bob using random basis each time
    • Bob measures these qubits using random basis
    • A qubit sent and received correctly if the basis match. They use this fact to arrive at common correctly received bits
    • But how do they discover correct matching? 
      • They can announce the basis after the process, they determine which positions of the key has same basis.
    • How do they know Eve has not read? 
      • They can compare on a subset of qubits, if their recovered bits are same in all bits in the subset then can conclude that Eve was not there. They can use the rest of bits as a shared key.
    7.2. Quantum Fourier Transform
    • DFT (Discrete Fourier Transform) 
      • map vectors from time domain to frequency domain
    • periodic function
    8. RSA and Shor Algorithms
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    1. Linear Algebra to Quantum Computing
    • 为什么学？ 
      • 解决经典计算机无法解决的问题
      • 对于量子纠错可能性的乐观主义
    • 这是什么？ 
      • 基于量子力学的计算机学说
      • 量子化学，量子深度学习
      • 后量子密码学
    • Physical Qubits: 
      • Particles with polarization(photon) 光子
      • Trapped ions 离子阱
      • Cold Atoms 冷原子
      • Quantum dots 量子点
      • Using Defect in Crystals 晶体缺陷
      • Superconductors 超导体
    • Major Players 
      • IBM, Google, Microsoft
      • D-Wave, uses Quantum annealing to solve optimization problems
    • Model of Quantum Computer 
      • Input
      • Quantum Processor on Cloud 
        • Prepare a state
        • Apply gates
        • Measure
      • Output: the probability distribution of the measurement
    2. Classical to Quantum Systems and Basic Quantum Theory
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  • spin 自旋
    • prototypical way to implement quantum bits of information, or qubits
    • clockwise/anticlockwise
  • transition amplitude 转移振幅
    • inner product
    • determine how likely a start state will change to an end state (after measurement)
  • entanglement 纠缠
    • connect two qubits using a gate
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  • max-cut 
    • edges have weights
    • find a cut that maximizes the sum of the weights of the edges that are cut
  • minimum vetec cover 
    • find the minimum number of vertices that cover all edges
  • simulated annealing 模拟退火 
    • a probabilistic technique for approximating the global optimum of a given function
  • local search (Monte Carlo) 
    • find solution from neighbors
    • ",4),gn=Q("li",null,"reproduce annealing process",-1),cn={class:"MathJax",jax:"SVG",style:{position:"relative"}},Hn={style:{"vertical-align":"-0.357ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.409ex",height:"1.895ex",role:"img",focusable:"false",viewBox:"0 -680 1065 837.8","aria-hidden":"true"},pn=T('',1),un=[pn],fn=Q("mjx-assistive-mml",{unselectable:"on",display:"inline"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[Q("msub",null,[Q("mi",null,"E"),Q("mi",null,"i")])])],-1),Mn={class:"MathJax",jax:"SVG",style:{position:"relative"}},Ln={style:{"vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"0.781ex",height:"1.52ex",role:"img",focusable:"false",viewBox:"0 -661 345 672","aria-hidden":"true"},Vn=Q("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[Q("g",{"data-mml-node":"math"},[Q("g",{"data-mml-node":"mi"},[Q("path",{"data-c":"1D456",d:"M184 600Q184 624 203 642T247 661Q265 661 277 649T290 619Q290 596 270 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    6.1. Ising Hamiltonian and Quantum Adiabatic Annealing
    • physical system generates optimal solution 
      • map optimization problem to physical system ... cool the system to zero ... obtain the lowest energy state
      • an observable is represented by Hermitian matrix ... its eigenvalues are the measured values ... diagonalize the matrix and eigenvalues are the diagonal elements
    • Adiabatic theorem 
      • if a system is in the ground state of a Hamiltonian, and the Hamiltonian is slowly changed, then the system will remain in the ground state
    6.2. QAOA
    • quantum approximate optimization algorithm 量子近似优化算法
    ',4),ie={class:"hint-container tip"},ce=T('
    Tips
    A gpt's explaination of QAOA:
    量子近似优化算法（Quantum Approximate Optimization Algorithm，QAOA）是一种混合量子经典算法，用于解决组合优化问题。QAOA 结合了量子计算的并行性和经典优化的灵活性，旨在找到近似最优解。以下是 QAOA 的高层次解释：
    QAOA 的基本概念
    QAOA 通过构造一个参数化的量子电路来近似求解组合优化问题。该算法的核心思想是通过调整参数，使得量子电路生成的量子态在测量后能够提供优化问题的近似最优解。
    QAOA 的步骤
    ',6),He=Q("p",null,[Q("strong",null,"问题表示"),m("：")],-1),pe=Q("li",null,"将组合优化问题表示为一个二次无约束二进制优化（QUBO）问题或等价的 Ising 模型。",-1),ue={class:"MathJax",jax:"SVG",style:{position:"relative"}},fe={style:{"vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.373ex",height:"1.005ex",role:"img",focusable:"false",viewBox:"0 -444 607 444","aria-hidden":"true"},Me=T('',1),Le=[Me],Ve=Q("mjx-assistive-mml",{unselectable:"on",display:"inline"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mi",{mathvariant:"bold"},"x")])])],-1),Ze={class:"MathJax",jax:"SVG",style:{position:"relative"}},xe={style:{"vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.853ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 2145 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    QAOA 的优点
    • 并行性：利用量子计算的并行性，可以同时探索多个解空间。
    • 灵活性：通过调整参数，可以适应不同的优化问题。
    • 近似最优解：即使在量子计算资源有限的情况下，QAOA 也能找到高质量的近似解。
    QAOA 的应用
    QAOA 可以应用于各种组合优化问题，包括但不限于：
    • 最大割问题（Max-Cut）
    • 旅行商问题（TSP）
    • 图着色问题
    • 集合覆盖问题
    • 资源分配问题
    ',5),gs=T('
    7.1. Quantum Key Distribution
    • Alice and Bob agree in advance a set with two different basis, say the z-basis or the x-basis
    • Alice sends encoded qubits to Bob using random basis each time
    • Bob measures these qubits using random basis
    • A qubit sent and received correctly if the basis match. They use this fact to arrive at common correctly received bits
    • But how do they discover correct matching? 
      • They can announce the basis after the process, they determine which positions of the key has same basis.
    • How do they know Eve has not read? 
      • They can compare on a subset of qubits, if their recovered bits are same in all bits in the subset then can conclude that Eve was not there. They can use the rest of bits as a shared key.
    7.2. Quantum Fourier Transform
    • DFT (Discrete Fourier Transform) 
      • map vectors from time domain to frequency domain
    • periodic function
    8. RSA and Shor Algorithms
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      3. \\n
    3. Sample Statistics
      4. \\n
    4. Point Estimation
      5. \\n
    5. Confidence
      6. \\n
    6. Confidence for Normals
      7. \\n
    7. Hypothesis Testing
      8. \\n
    8. Comparing Populations
      9. \\n
    9. Goodness of fit
      10. \\n
    10. Modelling Dependence
      11. \\n
    ","autoDesc":true}');export{d as comp,m as data};
+import{_ as t}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as e,o as i,a as s}from"./app-Cb1RROb0.js";const a={},o=s('
    What is this course about?
    The basic concepts of statistics that an engineer needs to know. The course covers the following topics:
    1. Bayesian Statistics
    2. Bayesian Estimation and Hypothesis Testing
    3. Sample Statistics
    4. Point Estimation
    5. Confidence
    6. Confidence for Normals
    7. Hypothesis Testing
    8. Comparing Populations
    9. Goodness of fit
    10. Modelling Dependence
    What is statistics?
    Statistics is the science of collecting, analyzing, and interpreting data. It is used to understand the world around us, make decisions, and solve problems. Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. It is used in a wide range of fields, including science, engineering, business, and social sciences.
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    What is this course about?
    \\n
    The basic concepts of statistics that an engineer needs to know. The course covers the following topics:
    \\n
      \\n
    1. Bayesian Statistics
      2. \\n
    2. Bayesian Estimation and Hypothesis Testing
      3. \\n
    3. Sample Statistics
      4. \\n
    4. Point Estimation
      5. \\n
    5. Confidence
      6. \\n
    6. Confidence for Normals
      7. \\n
    7. Hypothesis Testing
      8. \\n
    8. Comparing Populations
      9. \\n
    9. Goodness of fit
      10. \\n
    10. Modelling Dependence
      11. \\n
    ","autoDesc":true}');export{d as comp,m as data};
diff --git a/assets/ESTR1005.html-CZvG6sqq.js b/assets/ESTR1005.html-qWIXJocJ.js
similarity index 99%
rename from assets/ESTR1005.html-CZvG6sqq.js
rename to assets/ESTR1005.html-qWIXJocJ.js
index 39c53fc0..12254183 100644
--- a/assets/ESTR1005.html-CZvG6sqq.js
+++ b/assets/ESTR1005.html-qWIXJocJ.js
@@ -1 +1 @@
-import{_ as i}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as e,o as l,a as t}from"./app-CF0Bed9T.js";const a={},n=t('
    What is this course about?
    This course is an introduction to linear algebra for engineering students. The course covers the following topics:
    1. Matrices/Vectors
    • gaussian elimination
    • elementary row operations
    • rank/nullity
    • vector spaces
    • row/column/null spaces
    • systems of linear equations
    1. Determinants: Definitions/Properties
    • Cramer's rule
    • matrix inverses
    • Gauss-Jordan method/determinant method
    • linear transformations
    1. Eigenvalues/Eigenvectors/Applications
    • special matrices
    • diagonalization
    1. Basis Transformations/Eigenbasis Transformations
    • unitary matrices
    • Schur decomposition
    • spectral theorem for symmetric/hermitian/normal matrices
    • generalized eigenvectors
    • Jordan canonical form
    1. Fields
    • Euclid's algorithm
    • prime field
    • similarities/differences between finite fields and real/complex fields
    1. Applications of Finite Fields: Codes
    • polynomials over finite fields/Schwartz-Zippel lemma/applications
    • extension fields
    • finite field calculations on wxmaxima
    1. Sets of Numbers
    • limit of a sequence
    • Cauchy's convergence criterion
    • Bolzano-Weierstrass theorem
    • limits of functions
    • continuity of functions
    • continuity of functions in two variables
    1. Derivatives
    • mean-value theorems
    • Taylor's theorem
    • Riemann integral
    • numerical integration
    1. Vectors in 2-Space and 3-Space
    • definition(s) of dot-products
    • properties of dot-products
    • more about projections
    • vector cross product
    • vector cross product/scalar triple product: examples and applications
    • curves/surfaces/vector fields
    • scalar/vector functions and their derivatives
    • lengths of curves
    • arc-length parametrization of curves/applications
    1. Acceleration/Curvature/Torsion
    • coriolis acceleration
    • multivariate chain-rule/mean-value theorem
    • gradient/directional derivatives: definitions/examples
    • gradient/directional derivative: proofs
    • gradient descent
    • multivariate optimization via gradients
    • divergence/Laplacian
    • curl
    • (scalar) line integrals (of vector functions): definitions/basic examples
    • line integrals: of vector functions: properties/more examples
    • path in/dependence of line integrals: definition/potential function/examples
    • path in/dependence of line integrals: proofs regarding potential functions
    • double integrals: definitions, basic examples
    • double integrals: interchange of order of integration
    • change of variables/jacobian: definitions/examples
    • surfaces/parametrizations/surface normals/tangent planes
    • surface vector integrals
    • surface scalar integrals
    What is linear algebra?
    Linear algebra is the branch of mathematics that deals with vector spaces and linear mappings between these spaces. It is a fundamental part of modern mathematics and has many applications in science and engineering. Linear algebra is used in a wide range of fields, including physics, computer science, economics, and engineering. It is used to solve systems of linear equations, analyze data, and study the properties of geometric objects. Linear algebra is also used in computer graphics, machine learning, and cryptography.
    ',24),o=[n];function r(s,c){return l(),e("div",null,o)}const m=i(a,[["render",r],["__file","ESTR1005.html.vue"]]),d=JSON.parse('{"path":"/bachelor/ESTR1005.html","title":"ESTR1005 Linear Algebra for Engineers","lang":"en-US","frontmatter":{"title":"ESTR1005 Linear Algebra for Engineers","shortTitle":"ESTR1005","icon":"book-open","order":2,"category":["CUHK"],"tag":["Mathematics"],"description":"What is this course about? This course is an introduction to linear algebra for engineering students. The course covers the following topics: Matrices/Vectors gaussian eliminati...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR1005.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR1005 Linear Algebra for Engineers"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to linear algebra for engineering students. The course covers the following topics: Matrices/Vectors gaussian eliminati..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR1005 Linear Algebra for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is linear algebra?","slug":"what-is-linear-algebra","link":"#what-is-linear-algebra","children":[]}],"git":{"createdTime":1722554882000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":5},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR1005.md","localizedDate":"August 1, 2024","excerpt":"
    What is this course about?
    \\n
    This course is an introduction to linear algebra for engineering students. The course covers the following topics:
    \\n
      \\n
    1. Matrices/Vectors
      2. \\n
    \\n
      \\n
    • gaussian elimination
      • \\n
    • elementary row operations
      • \\n
    • rank/nullity
      • \\n
    • vector spaces
      • \\n
    • row/column/null spaces
      • \\n
    • systems of linear equations
      • \\n
    ","autoDesc":true}');export{m as comp,d as data};
+import{_ as i}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as e,o as l,a as t}from"./app-Cb1RROb0.js";const a={},n=t('
    What is this course about?
    This course is an introduction to linear algebra for engineering students. The course covers the following topics:
    1. Matrices/Vectors
    • gaussian elimination
    • elementary row operations
    • rank/nullity
    • vector spaces
    • row/column/null spaces
    • systems of linear equations
    1. Determinants: Definitions/Properties
    • Cramer's rule
    • matrix inverses
    • Gauss-Jordan method/determinant method
    • linear transformations
    1. Eigenvalues/Eigenvectors/Applications
    • special matrices
    • diagonalization
    1. Basis Transformations/Eigenbasis Transformations
    • unitary matrices
    • Schur decomposition
    • spectral theorem for symmetric/hermitian/normal matrices
    • generalized eigenvectors
    • Jordan canonical form
    1. Fields
    • Euclid's algorithm
    • prime field
    • similarities/differences between finite fields and real/complex fields
    1. Applications of Finite Fields: Codes
    • polynomials over finite fields/Schwartz-Zippel lemma/applications
    • extension fields
    • finite field calculations on wxmaxima
    1. Sets of Numbers
    • limit of a sequence
    • Cauchy's convergence criterion
    • Bolzano-Weierstrass theorem
    • limits of functions
    • continuity of functions
    • continuity of functions in two variables
    1. Derivatives
    • mean-value theorems
    • Taylor's theorem
    • Riemann integral
    • numerical integration
    1. Vectors in 2-Space and 3-Space
    • definition(s) of dot-products
    • properties of dot-products
    • more about projections
    • vector cross product
    • vector cross product/scalar triple product: examples and applications
    • curves/surfaces/vector fields
    • scalar/vector functions and their derivatives
    • lengths of curves
    • arc-length parametrization of curves/applications
    1. Acceleration/Curvature/Torsion
    • coriolis acceleration
    • multivariate chain-rule/mean-value theorem
    • gradient/directional derivatives: definitions/examples
    • gradient/directional derivative: proofs
    • gradient descent
    • multivariate optimization via gradients
    • divergence/Laplacian
    • curl
    • (scalar) line integrals (of vector functions): definitions/basic examples
    • line integrals: of vector functions: properties/more examples
    • path in/dependence of line integrals: definition/potential function/examples
    • path in/dependence of line integrals: proofs regarding potential functions
    • double integrals: definitions, basic examples
    • double integrals: interchange of order of integration
    • change of variables/jacobian: definitions/examples
    • surfaces/parametrizations/surface normals/tangent planes
    • surface vector integrals
    • surface scalar integrals
    What is linear algebra?
    Linear algebra is the branch of mathematics that deals with vector spaces and linear mappings between these spaces. It is a fundamental part of modern mathematics and has many applications in science and engineering. Linear algebra is used in a wide range of fields, including physics, computer science, economics, and engineering. It is used to solve systems of linear equations, analyze data, and study the properties of geometric objects. Linear algebra is also used in computer graphics, machine learning, and cryptography.
    ',24),o=[n];function r(s,c){return l(),e("div",null,o)}const m=i(a,[["render",r],["__file","ESTR1005.html.vue"]]),d=JSON.parse('{"path":"/bachelor/ESTR1005.html","title":"ESTR1005 Linear Algebra for Engineers","lang":"en-US","frontmatter":{"title":"ESTR1005 Linear Algebra for Engineers","shortTitle":"ESTR1005","icon":"book-open","order":2,"category":["CUHK"],"tag":["Mathematics"],"description":"What is this course about? This course is an introduction to linear algebra for engineering students. The course covers the following topics: Matrices/Vectors gaussian eliminati...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR1005.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR1005 Linear Algebra for Engineers"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to linear algebra for engineering students. The course covers the following topics: Matrices/Vectors gaussian eliminati..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR1005 Linear Algebra for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is linear algebra?","slug":"what-is-linear-algebra","link":"#what-is-linear-algebra","children":[]}],"git":{"createdTime":1722554882000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":5},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR1005.md","localizedDate":"August 1, 2024","excerpt":"
    What is this course about?
    \\n
    This course is an introduction to linear algebra for engineering students. The course covers the following topics:
    \\n
      \\n
    1. Matrices/Vectors
      2. \\n
    \\n
      \\n
    • gaussian elimination
      • \\n
    • elementary row operations
      • \\n
    • rank/nullity
      • \\n
    • vector spaces
      • \\n
    • row/column/null spaces
      • \\n
    • systems of linear equations
      • \\n
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diff --git a/assets/ESTR1006.html-FnoQi0hr.js b/assets/ESTR1006.html-Dh9wukQt.js
similarity index 98%
rename from assets/ESTR1006.html-FnoQi0hr.js
rename to assets/ESTR1006.html-Dh9wukQt.js
index 3efe198a..7db03be9 100644
--- a/assets/ESTR1006.html-FnoQi0hr.js
+++ b/assets/ESTR1006.html-Dh9wukQt.js
@@ -1 +1 @@
-import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as i,o as t,a as l}from"./app-CF0Bed9T.js";const a={},o=l('
    What is this course about?
    This course is an introduction to multivariable calculus for engineering students. The course covers the following topics:
    1. Matrix Operations and Vector Spaces
    • inverse of a matrix
    • Gauss-Jordan elimination
    • LU decomposition
    • vector spaces
    • inner product spaces
    • linear transformations
    1. Vectors and Vector Products
    • vectors in 2D/3D space
    • inner product
    • vector product
    1. Vector Fields and Curves
    • vector field
    • curves
    • arc length
    • torsion
    • chain rule
    1. Vector Calculus
    • gradient
    • directional derivative
    • potential function
    • divergence of a vector field
    • Laplacian
    • curl of a vector field
    1. Integration
    • line integrals
    • path independence of line integrals
    • double integrals
    • Green's theorem in the plane
    • Stokes' theorem
    1. Surface Geometry and Integrals
    • representation of surfaces
    • cylinder/sphere/cone
    • tangent plane and surface normal
    • surface integrals
    • orientation of surfaces
    1. Triple Integrals
    • divergence theorem of Gauss
    What is multivariable calculus?
    Multivariable calculus is the extension of calculus in one variable to calculus in more than one variable. It is a branch of calculus that studies functions of more than one variable. It is used in many fields of science and engineering, especially in physics and engineering. Multivariable calculus is also known as vector calculus, as it deals with vectors and vector functions.
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    What is this course about?
    \\n
    This course is an introduction to multivariable calculus for engineering students. The course covers the following topics:
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      \\n
    1. Matrix Operations and Vector Spaces
      2. \\n
    \\n
      \\n
    • inverse of a matrix
      • \\n
    • Gauss-Jordan elimination
      • \\n
    • LU decomposition
      • \\n
    • vector spaces
      • \\n
    • inner product spaces
      • \\n
    • linear transformations
      • \\n
    ","autoDesc":true}');export{p as comp,d as data};
+import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as i,o as t,a as l}from"./app-Cb1RROb0.js";const a={},o=l('
    What is this course about?
    This course is an introduction to multivariable calculus for engineering students. The course covers the following topics:
    1. Matrix Operations and Vector Spaces
    • inverse of a matrix
    • Gauss-Jordan elimination
    • LU decomposition
    • vector spaces
    • inner product spaces
    • linear transformations
    1. Vectors and Vector Products
    • vectors in 2D/3D space
    • inner product
    • vector product
    1. Vector Fields and Curves
    • vector field
    • curves
    • arc length
    • torsion
    • chain rule
    1. Vector Calculus
    • gradient
    • directional derivative
    • potential function
    • divergence of a vector field
    • Laplacian
    • curl of a vector field
    1. Integration
    • line integrals
    • path independence of line integrals
    • double integrals
    • Green's theorem in the plane
    • Stokes' theorem
    1. Surface Geometry and Integrals
    • representation of surfaces
    • cylinder/sphere/cone
    • tangent plane and surface normal
    • surface integrals
    • orientation of surfaces
    1. Triple Integrals
    • divergence theorem of Gauss
    What is multivariable calculus?
    Multivariable calculus is the extension of calculus in one variable to calculus in more than one variable. It is a branch of calculus that studies functions of more than one variable. It is used in many fields of science and engineering, especially in physics and engineering. Multivariable calculus is also known as vector calculus, as it deals with vectors and vector functions.
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    What is this course about?
    \\n
    This course is an introduction to multivariable calculus for engineering students. The course covers the following topics:
    \\n
      \\n
    1. Matrix Operations and Vector Spaces
      2. \\n
    \\n
      \\n
    • inverse of a matrix
      • \\n
    • Gauss-Jordan elimination
      • \\n
    • LU decomposition
      • \\n
    • vector spaces
      • \\n
    • inner product spaces
      • \\n
    • linear transformations
      • \\n
    ","autoDesc":true}');export{p as comp,d as data};
diff --git a/assets/ESTR2004.html-BZOxIpzH.js b/assets/ESTR2004.html-DNa6n759.js
similarity index 99%
rename from assets/ESTR2004.html-BZOxIpzH.js
rename to assets/ESTR2004.html-DNa6n759.js
index 200f90d5..a84ed5f3 100644
--- a/assets/ESTR2004.html-BZOxIpzH.js
+++ b/assets/ESTR2004.html-DNa6n759.js
@@ -1 +1 @@
-import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as i,a as o}from"./app-CF0Bed9T.js";const s={},a=o('
    Does any resource help with this course?
    Textbook: Mathematics for Computer Science
    What is this course about?
    This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
    1. Introduction
    • mathematical induction
    1. Summation
    • multiple summation
    • floor and ceiling
    • Akra-Bazzi theorem
    • inexact summation
    • Euler summation formula
    1. Asymptotics
    • tower of hanoi
    • big-O, big-Ω, big-Θ
    • divide-and-conquer algorithms
    1. Recurrences and Generating Functions
    • recurrences
    • repertoire method
    • Josephus problem
    • generating functions
    1. Sets and Combinatorial Counting
    • set theory
    • multiplication principle
    • subtraction and division principles
    • combinatorial proofs
    • distribute objects to people
    1. Elements of discrete probability
    • multinomial theorem
    • arrival problems
    • inclusion-exclusion principle
    • convolution, generalized binomial theorem
    • parenthesizing expressions
    1. Introduction to Graph Theory
    • handshaking lemma
    • paths, cycles, connectedness
    • properties of trees
    • proving graph properties with induction
    • complete and bipartite graphs
    • matching
    • Hall's theorem
    • verifying Hall's condition
    What is discrete mathematics?
    Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic – do not vary smoothly in this way, but have distinct, separated values. Discrete mathematics therefore excludes topics in "continuous mathematics" such as calculus or Euclidean geometry. Discrete objects can often be enumerated by integers. More formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets (sets that have the same cardinality as subsets of the natural numbers, including rational numbers but not real numbers). However, there is no exact definition of the term "discrete mathematics." Indeed, discrete mathematics is described less by what is included than by what is excluded: continuously varying quantities and related notions.
    How the mathematical problem is proved?
    1. Direct Proof: A direct proof is a method of proof that establishes the truth or validity of a statement by logically showing that it must follow from a given set of assumptions. It is a straightforward method of proof that is used when the conclusion is a direct result of the hypothesis.
    2. Proof by Contradiction: Proof by contradiction is a method of proof that establishes the truth or validity of a statement by showing that the negation of the statement leads to a contradiction. It is a powerful method of proof that is used when direct proof is not possible or when it is easier to prove the negation of the statement.
    3. Proof by Induction: Proof by induction is a method of proof that establishes the truth or validity of a statement by showing that it holds for a base case and that if it holds for any case, then it must hold for the next case. It is a method of proof that is used to prove statements about natural numbers or other recursively defined objects.
    ',22),r=[a];function n(l,h){return i(),t("div",null,r)}const u=e(s,[["render",n],["__file","ESTR2004.html.vue"]]),d=JSON.parse('{"path":"/bachelor/ESTR2004.html","title":"ESTR2004 Discrete Mathematics for Engineers","lang":"en-US","frontmatter":{"title":"ESTR2004 Discrete Mathematics for Engineers","shortTitle":"ESTR2004","icon":"book-open","order":3,"category":["CUHK"],"tag":["Mathematics"],"description":"Does any resource help with this course? Textbook: Mathematics for Computer Science What is this course about? This course is an introduction to discrete mathematics for enginee...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR2004.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR2004 Discrete Mathematics for Engineers"}],["meta",{"property":"og:description","content":"Does any resource help with this course? Textbook: Mathematics for Computer Science What is this course about? This course is an introduction to discrete mathematics for enginee..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR2004 Discrete Mathematics for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"Does any resource help with this course?","slug":"does-any-resource-help-with-this-course","link":"#does-any-resource-help-with-this-course","children":[]},{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is discrete mathematics?","slug":"what-is-discrete-mathematics","link":"#what-is-discrete-mathematics","children":[]},{"level":3,"title":"How the mathematical problem is proved?","slug":"how-the-mathematical-problem-is-proved","link":"#how-the-mathematical-problem-is-proved","children":[]}],"git":{"createdTime":1722557020000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":3},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR2004.md","localizedDate":"August 2, 2024","excerpt":"
    Does any resource help with this course?
    \\n
    Textbook: Mathematics for Computer Science
    \\n
    What is this course about?
    \\n
    This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
    ","autoDesc":true}');export{u as comp,d as data};
+import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as i,a as o}from"./app-Cb1RROb0.js";const s={},a=o('
    Does any resource help with this course?
    Textbook: Mathematics for Computer Science
    What is this course about?
    This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
    1. Introduction
    • mathematical induction
    1. Summation
    • multiple summation
    • floor and ceiling
    • Akra-Bazzi theorem
    • inexact summation
    • Euler summation formula
    1. Asymptotics
    • tower of hanoi
    • big-O, big-Ω, big-Θ
    • divide-and-conquer algorithms
    1. Recurrences and Generating Functions
    • recurrences
    • repertoire method
    • Josephus problem
    • generating functions
    1. Sets and Combinatorial Counting
    • set theory
    • multiplication principle
    • subtraction and division principles
    • combinatorial proofs
    • distribute objects to people
    1. Elements of discrete probability
    • multinomial theorem
    • arrival problems
    • inclusion-exclusion principle
    • convolution, generalized binomial theorem
    • parenthesizing expressions
    1. Introduction to Graph Theory
    • handshaking lemma
    • paths, cycles, connectedness
    • properties of trees
    • proving graph properties with induction
    • complete and bipartite graphs
    • matching
    • Hall's theorem
    • verifying Hall's condition
    What is discrete mathematics?
    Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic – do not vary smoothly in this way, but have distinct, separated values. Discrete mathematics therefore excludes topics in "continuous mathematics" such as calculus or Euclidean geometry. Discrete objects can often be enumerated by integers. More formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets (sets that have the same cardinality as subsets of the natural numbers, including rational numbers but not real numbers). However, there is no exact definition of the term "discrete mathematics." Indeed, discrete mathematics is described less by what is included than by what is excluded: continuously varying quantities and related notions.
    How the mathematical problem is proved?
    1. Direct Proof: A direct proof is a method of proof that establishes the truth or validity of a statement by logically showing that it must follow from a given set of assumptions. It is a straightforward method of proof that is used when the conclusion is a direct result of the hypothesis.
    2. Proof by Contradiction: Proof by contradiction is a method of proof that establishes the truth or validity of a statement by showing that the negation of the statement leads to a contradiction. It is a powerful method of proof that is used when direct proof is not possible or when it is easier to prove the negation of the statement.
    3. Proof by Induction: Proof by induction is a method of proof that establishes the truth or validity of a statement by showing that it holds for a base case and that if it holds for any case, then it must hold for the next case. It is a method of proof that is used to prove statements about natural numbers or other recursively defined objects.
    ',22),r=[a];function n(l,h){return i(),t("div",null,r)}const u=e(s,[["render",n],["__file","ESTR2004.html.vue"]]),d=JSON.parse('{"path":"/bachelor/ESTR2004.html","title":"ESTR2004 Discrete Mathematics for Engineers","lang":"en-US","frontmatter":{"title":"ESTR2004 Discrete Mathematics for Engineers","shortTitle":"ESTR2004","icon":"book-open","order":3,"category":["CUHK"],"tag":["Mathematics"],"description":"Does any resource help with this course? Textbook: Mathematics for Computer Science What is this course about? This course is an introduction to discrete mathematics for enginee...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR2004.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR2004 Discrete Mathematics for Engineers"}],["meta",{"property":"og:description","content":"Does any resource help with this course? Textbook: Mathematics for Computer Science What is this course about? This course is an introduction to discrete mathematics for enginee..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR2004 Discrete Mathematics for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"Does any resource help with this course?","slug":"does-any-resource-help-with-this-course","link":"#does-any-resource-help-with-this-course","children":[]},{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is discrete mathematics?","slug":"what-is-discrete-mathematics","link":"#what-is-discrete-mathematics","children":[]},{"level":3,"title":"How the mathematical problem is proved?","slug":"how-the-mathematical-problem-is-proved","link":"#how-the-mathematical-problem-is-proved","children":[]}],"git":{"createdTime":1722557020000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":3},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR2004.md","localizedDate":"August 2, 2024","excerpt":"
    Does any resource help with this course?
    \\n
    Textbook: Mathematics for Computer Science
    \\n
    What is this course about?
    \\n
    This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
    ","autoDesc":true}');export{u as comp,d as data};
diff --git a/assets/ESTR2018.html-CiOZHPVr.js b/assets/ESTR2018.html-DzSpOEQx.js
similarity index 98%
rename from assets/ESTR2018.html-CiOZHPVr.js
rename to assets/ESTR2018.html-DzSpOEQx.js
index 1e265017..4ebe353d 100644
--- a/assets/ESTR2018.html-CiOZHPVr.js
+++ b/assets/ESTR2018.html-DzSpOEQx.js
@@ -1 +1 @@
-import{_ as i}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as e,o as t,a as o}from"./app-CF0Bed9T.js";const n={},a=o('
    What is this course about?
    This course is an introduction to probability for engineering students. The course covers the following topics:
    1. Probability and Counting
    2. Axioms of Probability. Conditional Probabilities
    3. Conditional Probability and Independence
    4. Random Variables
    5. Expectation, Variance, Joint PMFs
    6. Conditional PMFs and Independent Random Variables
    7. Continuous Random Variables
    8. Multiple Continuous Random Variables
    9. Continuous Bayes’ rule, Derived Random Variables
    10. Correlation and Large Deviations
    11. Chebyshev’s Inequality and The Central Limit Theorem
    What is probability?
    Probability is the likelihood of an event occurring. It is a measure of the likelihood that a given event will occur. Probability is a branch of mathematics that deals with the study of random events. It is used to predict the likelihood of future events and to analyze the past. Probability is used in a wide range of fields, including science, engineering, business, and social sciences.
    ',5),l=[a];function r(s,c){return t(),e("div",null,l)}const u=i(n,[["render",r],["__file","ESTR2018.html.vue"]]),b=JSON.parse('{"path":"/bachelor/ESTR2018.html","title":"ESTR2018 Probability for Engineers","lang":"en-US","frontmatter":{"title":"ESTR2018 Probability for Engineers","shortTitle":"ESTR2018","icon":"book-open","order":3,"category":["CUHK"],"tag":["Mathematics"],"description":"What is this course about? This course is an introduction to probability for engineering students. The course covers the following topics: Probability and Counting Axioms of Pro...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR2018.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR2018 Probability for Engineers"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to probability for engineering students. The course covers the following topics: Probability and Counting Axioms of Pro..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR2018 Probability for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is probability?","slug":"what-is-probability","link":"#what-is-probability","children":[]}],"git":{"createdTime":1722557020000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":3},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR2018.md","localizedDate":"August 2, 2024","excerpt":"
    What is this course about?
    \\n
    This course is an introduction to probability for engineering students. The course covers the following topics:
    \\n
      \\n
    1. Probability and Counting
      2. \\n
    2. Axioms of Probability. Conditional Probabilities
      3. \\n
    3. Conditional Probability and Independence
      4. \\n
    4. Random Variables
      5. \\n
    5. Expectation, Variance, Joint PMFs
      6. \\n
    6. Conditional PMFs and Independent Random Variables
      7. \\n
    7. Continuous Random Variables
      8. \\n
    8. Multiple Continuous Random Variables
      9. \\n
    9. Continuous Bayes’ rule, Derived Random Variables
      10. \\n
    10. Correlation and Large Deviations
      11. \\n
    11. Chebyshev’s Inequality and The Central Limit Theorem
      12. \\n
    ","autoDesc":true}');export{u as comp,b as data};
+import{_ as i}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as e,o as t,a as o}from"./app-Cb1RROb0.js";const n={},a=o('
    What is this course about?
    This course is an introduction to probability for engineering students. The course covers the following topics:
    1. Probability and Counting
    2. Axioms of Probability. Conditional Probabilities
    3. Conditional Probability and Independence
    4. Random Variables
    5. Expectation, Variance, Joint PMFs
    6. Conditional PMFs and Independent Random Variables
    7. Continuous Random Variables
    8. Multiple Continuous Random Variables
    9. Continuous Bayes’ rule, Derived Random Variables
    10. Correlation and Large Deviations
    11. Chebyshev’s Inequality and The Central Limit Theorem
    What is probability?
    Probability is the likelihood of an event occurring. It is a measure of the likelihood that a given event will occur. Probability is a branch of mathematics that deals with the study of random events. It is used to predict the likelihood of future events and to analyze the past. Probability is used in a wide range of fields, including science, engineering, business, and social sciences.
    ',5),l=[a];function r(s,c){return t(),e("div",null,l)}const u=i(n,[["render",r],["__file","ESTR2018.html.vue"]]),b=JSON.parse('{"path":"/bachelor/ESTR2018.html","title":"ESTR2018 Probability for Engineers","lang":"en-US","frontmatter":{"title":"ESTR2018 Probability for Engineers","shortTitle":"ESTR2018","icon":"book-open","order":3,"category":["CUHK"],"tag":["Mathematics"],"description":"What is this course about? This course is an introduction to probability for engineering students. The course covers the following topics: Probability and Counting Axioms of Pro...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/bachelor/ESTR2018.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"ESTR2018 Probability for Engineers"}],["meta",{"property":"og:description","content":"What is this course about? This course is an introduction to probability for engineering students. The course covers the following topics: Probability and Counting Axioms of Pro..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Mathematics"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"ESTR2018 Probability for Engineers\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]},{"level":3,"title":"What is probability?","slug":"what-is-probability","link":"#what-is-probability","children":[]}],"git":{"createdTime":1722557020000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"yusihong073@gmail.com","commits":3},{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":1}]},"filePathRelative":"bachelor/ESTR2018.md","localizedDate":"August 2, 2024","excerpt":"
    What is this course about?
    \\n
    This course is an introduction to probability for engineering students. The course covers the following topics:
    \\n
      \\n
    1. Probability and Counting
      2. \\n
    2. Axioms of Probability. Conditional Probabilities
      3. \\n
    3. Conditional Probability and Independence
      4. \\n
    4. Random Variables
      5. \\n
    5. Expectation, Variance, Joint PMFs
      6. \\n
    6. Conditional PMFs and Independent Random Variables
      7. \\n
    7. Continuous Random Variables
      8. \\n
    8. Multiple Continuous Random Variables
      9. \\n
    9. Continuous Bayes’ rule, Derived Random Variables
      10. \\n
    10. Correlation and Large Deviations
      11. \\n
    11. Chebyshev’s Inequality and The Central Limit Theorem
      12. \\n
    ","autoDesc":true}');export{u as comp,b as data};
diff --git a/assets/GEOM90008.html-xuKmgyDj.js b/assets/GEOM90008.html-CLsDvEgG.js
similarity index 98%
rename from assets/GEOM90008.html-xuKmgyDj.js
rename to assets/GEOM90008.html-CLsDvEgG.js
index 364b9e51..7a742f32 100644
--- a/assets/GEOM90008.html-xuKmgyDj.js
+++ b/assets/GEOM90008.html-CLsDvEgG.js
@@ -1 +1 @@
-import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as a,a as i}from"./app-CF0Bed9T.js";const o={},s=i('
    Does any resource help with this course?
    Textbook: Spatial Data Management
    What is this course about?
    This course is an introduction to spatial data management for engineering students. The course covers the following topics:
    1. Data Modelling for Geospatial Applications
    • spatial data modelling
    • requirements analysis
    • conceptual modelling
    • introduction to databases
    • project briefing
    • logical database design
    • maintaining integrity
    • physical db design
    • spatial data representation
    • spatial reference systems
    • spatial reference systems - transformations and projections
    1. Querying and Analysing Spatial Data
    • vector geometries
    • querying spatial data
    • geometrical analysis
    • topological analysis
    • network models and analysis
    • raster data handling and analysis
    1. Efficient Data Access
    • effective spatial data access
    • space-driven indexing
    • object-driven indexes
    ',10),n=[s];function l(r,c){return a(),t("div",null,n)}const u=e(o,[["render",l],["__file","GEOM90008.html.vue"]]),m=JSON.parse('{"path":"/master/GEOM90008.html","title":"GEOM90008 Spatial Data Management","lang":"en-US","frontmatter":{"title":"GEOM90008 Spatial Data Management","shortTitle":"GEOM90008","order":1,"icon":"book-open","category":["UniMelb","24S1"],"tag":["Database"],"description":"Does any resource help with this course? Textbook: Spatial Data Management What is this course about? This course is an introduction to spatial data management for engineering s...","head":[["meta",{"property":"og:url","content":"https://shyu216.github.io/knownoevil/knownoevil/master/GEOM90008.html"}],["meta",{"property":"og:site_name","content":"Know No Evil"}],["meta",{"property":"og:title","content":"GEOM90008 Spatial Data Management"}],["meta",{"property":"og:description","content":"Does any resource help with this course? Textbook: Spatial Data Management What is this course about? This course is an introduction to spatial data management for engineering s..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-10-10T03:31:47.000Z"}],["meta",{"property":"article:author","content":"Dale"}],["meta",{"property":"article:tag","content":"Database"}],["meta",{"property":"article:modified_time","content":"2024-10-10T03:31:47.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"GEOM90008 Spatial Data Management\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-10-10T03:31:47.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Dale\\",\\"url\\":\\"https://github.com/shyu216\\"}]}"]]},"headers":[{"level":3,"title":"Does any resource help with this course?","slug":"does-any-resource-help-with-this-course","link":"#does-any-resource-help-with-this-course","children":[]},{"level":3,"title":"What is this course about?","slug":"what-is-this-course-about","link":"#what-is-this-course-about","children":[]}],"git":{"createdTime":1722491295000,"updatedTime":1728531107000,"contributors":[{"name":"shyu216","email":"sihong1@student.unimelb.edu.au","commits":3},{"name":"shyu216","email":"yusihong073@gmail.com","commits":2}]},"filePathRelative":"master/GEOM90008.md","localizedDate":"August 1, 2024","excerpt":"
    Does any resource help with this course?
    \\n
    Textbook: Spatial Data Management
    \\n
    What is this course about?
    \\n
    This course is an introduction to spatial data management for engineering students. The course covers the following topics:
    ","autoDesc":true}');export{u as comp,m as data};
+import{_ as e}from"./plugin-vue_export-helper-DlAUqK2U.js";import{c as t,o as a,a as i}from"./app-Cb1RROb0.js";const o={},s=i('
    Does any resource help with this course?
    Textbook: Spatial Data Management
    What is this course about?
    This course is an introduction to spatial data management for engineering students. The course covers the following topics:
    1. Data Modelling for Geospatial Applications
    • spatial data modelling
    • requirements analysis
    • conceptual modelling
    • introduction to databases
    • project briefing
    • logical database design
    • maintaining integrity
    • physical db design
    • spatial data representation
    • spatial reference systems
    • spatial reference systems - transformations and projections
    1. Querying and Analysing Spatial Data
    • vector geometries
    • querying spatial data
    • geometrical analysis
    • topological analysis
    • network models and analysis
    • raster data handling and analysis
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    • space-driven indexing
    • object-driven indexes
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similarity index 98%
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--- a/assets/MATH1510.html-BCcgjFfK.js
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    This course is an introduction to calculus for engineering students. The course covers the following topics:
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    • vector algebra
    • functions and their representations
    1. Limits and Continuity
    • the limit of a function
    • continuity
    • limits involving infinity
    1. Derivatives
    • derivatives and rate of changes
    • differentiability
    • derivative of trigonometric functions
    • derivative of exponential functions & logarithmic functions
    • implicit differentiation
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    • polar coordinates
    • inverse trigonometric functions
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    • volume of a solid
    1. Partial Differentiations and Double Integrals
    • partial differentiations
    • double integrals
    1. Fourier Series
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    Does any resource help with this course?
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    • vector algebra
    • functions and their representations
    1. Limits and Continuity
    • the limit of a function
    • continuity
    • limits involving infinity
    1. Derivatives
    • derivatives and rate of changes
    • differentiability
    • derivative of trigonometric functions
    • derivative of exponential functions & logarithmic functions
    • implicit differentiation
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    • inverse trigonometric functions
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    • curve sketching
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    • indeterminate forms and L'Hopital's rule
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    • sigma notation, riemann sums and distances
    • the definite integrals
    • the fundamental theorem of calculus
    • the substitution rule
    • integration by parts
    • trigonometric integrals and substitution
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    • applications of integrals: area between curves
    • volume of a solid
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    • double integrals
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diff --git a/assets/app-CF0Bed9T.js b/assets/app-Cb1RROb0.js
similarity index 82%
rename from assets/app-CF0Bed9T.js
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index 49249869..7254ad6f 100644
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+++ b/assets/app-Cb1RROb0.js
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    What is this course about?
    
 
    The basic concepts of statistics that an engineer needs to know. The course covers the following topics:
    
 
      
 
    1. Bayesian Statistics
      2. 
@@ -28,7 +28,7 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
    2. Comparing Populations
      3. 
 
    3. Goodness of fit
      4. 
 
    4. Modelling Dependence
      5. 
-
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    What is this course about?
    
+`,t:"ENGG2780 Statistics for Engineers",s:"ENGG2780",i:"book-open",O:4,y:"a"}}],["/bachelor/ESTR1005.html",{loader:()=>U(()=>import("./ESTR1005.html-qWIXJocJ.js"),__vite__mapDeps([3,1])),meta:{d:1722554882e3,c:["CUHK"],g:["Mathematics"],e:`
    What is this course about?
    
 
    This course is an introduction to linear algebra for engineering students. The course covers the following topics:
    
 
      
 
    1. Matrices/Vectors
      2. 
@@ -40,7 +40,7 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
    2. vector spaces
      3. 
 
    3. row/column/null spaces
      4. 
 
    4. systems of linear equations
      5. 
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      What is this course about?
      
+`,t:"ESTR1005 Linear Algebra for Engineers",s:"ESTR1005",i:"book-open",O:2,y:"a"}}],["/bachelor/ESTR1006.html",{loader:()=>U(()=>import("./ESTR1006.html-Dh9wukQt.js"),__vite__mapDeps([4,1])),meta:{d:172255702e4,c:["CUHK"],g:["Mathematics"],e:`
      What is this course about?
      
 
      This course is an introduction to multivariable calculus for engineering students. The course covers the following topics:
      
 
        
 
      1. Matrix Operations and Vector Spaces
        2. 
@@ -52,10 +52,10 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
      2. vector spaces
        3. 
 
      3. inner product spaces
        4. 
 
      4. linear transformations
        5. 
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        Does any resource help with this course?
        
+`,t:"ESTR1006 Multivariable Calculus for Engineers",s:"ESTR1006",i:"book-open",O:2,y:"a"}}],["/bachelor/ESTR2004.html",{loader:()=>U(()=>import("./ESTR2004.html-DNa6n759.js"),__vite__mapDeps([5,1])),meta:{d:172255702e4,c:["CUHK"],g:["Mathematics"],e:`
        Does any resource help with this course?
        
 
        Textbook: Mathematics for Computer Science
        
 
        What is this course about?
        
-
        This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
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        What is this course about?
        
+
        This course is an introduction to discrete mathematics for engineering students. The course covers the following topics:
        `,t:"ESTR2004 Discrete Mathematics for Engineers",s:"ESTR2004",i:"book-open",O:3,y:"a"}}],["/bachelor/ESTR2018.html",{loader:()=>U(()=>import("./ESTR2018.html-DzSpOEQx.js"),__vite__mapDeps([6,1])),meta:{d:172255702e4,c:["CUHK"],g:["Mathematics"],e:`
        What is this course about?
        
 
        This course is an introduction to probability for engineering students. The course covers the following topics:
        
 
          
 
        1. Probability and Counting
          2. 
@@ -69,25 +69,12 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
        2. Continuous Bayes’ rule, Derived Random Variables
          3. 
 
        3. Correlation and Large Deviations
          4. 
 
        4. Chebyshev’s Inequality and The Central Limit Theorem
          5. 
-
        `,t:"ESTR2018 Probability for Engineers",s:"ESTR2018",i:"book-open",O:3,y:"a"}}],["/bachelor/MATH1510.html",{loader:()=>U(()=>import("./MATH1510.html-BCcgjFfK.js"),__vite__mapDeps([7,1])),meta:{d:1722554882e3,c:["CUHK"],g:["Mathematics"],e:`
        Does any resource help with this course?
        
+
      `,t:"ESTR2018 Probability for Engineers",s:"ESTR2018",i:"book-open",O:3,y:"a"}}],["/bachelor/MATH1510.html",{loader:()=>U(()=>import("./MATH1510.html-DlXoqcoE.js"),__vite__mapDeps([7,1])),meta:{d:1722554882e3,c:["CUHK"],g:["Mathematics"],e:`
      Does any resource help with this course?
      
 `,t:"MATH1510 Calculus for Engineers",s:"MATH1510",i:"book-open",O:1,y:"a"}}],["/bachelor/",{loader:()=>U(()=>import("./index.html-A_vb5YXN.js"),__vite__mapDeps([8,1])),meta:{d:1722489195e3,c:["CUHK"],e:`
      Dale took his Bachelor's degree at CUHK. Here are some thoughts and notes.
      
-`,t:"Bachelor",i:"book",y:"a"}}],["/other/",{loader:()=>U(()=>import("./index.html-BsohQDUE.js"),__vite__mapDeps([9,1])),meta:{d:172864891e4,e:`
      Dale randomly leaves some notes here.
      
-`,t:"Other",i:"comment",y:"a"}}],["/other/hackathon.html",{loader:()=>U(()=>import("./hackathon.html-DYxyOCrS.js"),__vite__mapDeps([10,1])),meta:{d:1728813271e3,c:["Hackathon"],g:["Event"],e:`
      What is the solution?
      
-
        
-
      • Greenwashing AI fact-check agent
        • 
-
      
-
      What is the topic of the project?
      
-
        
-
      • Sustainable decision making for consumers and investors
        • 
-
      
-
      What is the specific domain of the project?
      
-
        
-
      • Greenwashing
        • 
-
      
-
      What is the background of the project?
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      Prerequisites 1. Introduction to Genomics 基因组学
      
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      Dale took his Bachelor's degree at CUHK. Here are some thoughts and notes.
      
+`,t:"Bachelor",i:"book",y:"a"}}],["/master/COMP90014.html",{loader:()=>U(()=>import("./COMP90014.html-NIUEdyzc.js"),__vite__mapDeps([9,1])),meta:{d:1722491295e3,c:["UniMelb","24S2"],g:["Algorithm","Bioinformatics"],e:`
      Prerequisites 1. Introduction to Genomics 基因组学
      
 
        
 
      • eukaryotes, prokaryotes, viruses
 
          
@@ -243,9 +230,9 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
 
        
 
        • 
-
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      Does any resource help with this course?
      
+`,t:"COMP90014 Algorithms for Bioinformatics",s:"COMP90014",i:"book-open",O:2,y:"a"}}],["/master/COMP90018.html",{loader:()=>U(()=>import("./COMP90018.html-CrMJZzLo.js"),__vite__mapDeps([10,1])),meta:{d:172248583e4,c:["UniMelb","24S2"],g:["Mobile","Programming"],e:`
      Does any resource help with this course?
      
 
      Ubiquitous Computing Fundamentals
      
-
      What is this course about?
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      What is this course about?
      
+
      What is this course about?
      `,t:"COMP90018 Mobile Computing Systems Programming",s:"COMP90018",i:"book-open",O:2,y:"a"}}],["/master/COMP90042.html",{loader:()=>U(()=>import("./COMP90042.html-D1Qw8SnG.js"),__vite__mapDeps([11,1])),meta:{d:1722491295e3,c:["UniMelb","24S1"],g:["Artificial Intelligence","Machine Learning","Deep Learning","Natural Language Processing"],e:`
      What is this course about?
      
 
      This course is an introduction to natural language processing for engineering students. The course covers the following topics:
      
 
        
 
      1. Preprocessing
        2. 
@@ -260,35 +247,48 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
 
 
      2. stopword removal
        3. 
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        What is this course about?
        
+`,t:"COMP90042 Natural Language Processing",s:"COMP90042",i:"book-open",O:1,y:"a"}}],["/master/COMP90044.html",{loader:()=>U(()=>import("./COMP90044.html-CA-SqVVE.js"),__vite__mapDeps([12,1])),meta:{d:1722491295e3,c:["UniMelb","24S2"],g:["Research"],e:`
        What is this course about?
        
 
        This course is an introduction to research methods for computer science students.
        
 
        What is research?
        
-
        In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.
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        Does any resource help with this course?
        
+
        In a research domain, come up with a research question, and then find the answer to the question. A good statement of the research question is the half of the answer.
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        Does any resource help with this course?
        
 `,t:"COMP90054 AI Planning for Autonomy",s:"COMP90054",i:"book-open",O:1,y:"a"}}],["/master/COMP90077.html",{loader:()=>U(()=>import("./COMP90077.html-BTn3mtzf.js"),__vite__mapDeps([16,1])),meta:{d:1722491295e3,c:["UniMelb","24S1"],g:["Data Structures","Algorithm","Graph Theory"],e:`
        Does any resource help with this course?
        
+`,t:"COMP90054 AI Planning for Autonomy",s:"COMP90054",i:"book-open",O:1,y:"a"}}],["/master/COMP90077.html",{loader:()=>U(()=>import("./COMP90077.html-3qbhTuUP.js"),__vite__mapDeps([14,1])),meta:{d:1722491295e3,c:["UniMelb","24S1"],g:["Data Structures","Algorithm","Graph Theory"],e:`
        Does any resource help with this course?
        
 
          
 
        1. Algorithm Design by Kleinberg and Tardos
          2. 
 
        2. Algorithms by Jeff Erickson
          3. 
 
        3. Linear Programming
          4. 
 
        4. Design of Approximation Algorithms
          5. 
-
        `,t:"COMP90077 Advanced Algorithms and Data Structures",s:"COMP90077",i:"book-open",O:1,y:"a"}}],["/master/COMP90084.html",{loader:()=>U(()=>import("./COMP90084.html-OSmUPhgM.js"),__vite__mapDeps([17,1])),meta:{d:172248583e4,c:["UniMelb","24S2"],g:["Quantum Computing","Principles","Linear Algebra","Complex Numbers"],e:`
          
+
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        1. Matrix Cookbook
          2. 
 
        2. Quantum Computing for Computer Scientists
          3. 
-
        `,t:"COMP90084 Quantum Software Fundamentals",s:"COMP90084",i:"book-open",O:2,y:"a"}}],["/master/GEOM90008.html",{loader:()=>U(()=>import("./GEOM90008.html-xuKmgyDj.js"),__vite__mapDeps([18,1])),meta:{d:1722491295e3,c:["UniMelb","24S1"],g:["Database"],e:`
        Does any resource help with this course?
        
+
      `,t:"COMP90084 Quantum Software Fundamentals",s:"COMP90084",i:"book-open",O:2,y:"a"}}],["/master/GEOM90008.html",{loader:()=>U(()=>import("./GEOM90008.html-CLsDvEgG.js"),__vite__mapDeps([16,1])),meta:{d:1722491295e3,c:["UniMelb","24S1"],g:["Database"],e:`
      Does any resource help with this course?
      
 
      Textbook: Spatial Data Management
      
 
      What is this course about?
      
-
      This course is an introduction to spatial data management for engineering students. The course covers the following topics:
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      Dale took his Master's degree at UniMelb. Here are some thoughts and notes.
      
-`,t:"Master",i:"book",y:"a"}}],["/master/COMP90084/",{loader:()=>U(()=>import("./index.html-AyodEczY.js"),__vite__mapDeps([20,1])),meta:{d:173948885e4,c:["UniMelb"],e:`
      Here are some notes for COMP90084.
      
-`,t:"COMP90084",i:"bookmark",y:"a"}}],["/master/COMP90084/quantum.html",{loader:()=>U(()=>import("./quantum.html-BVa6HuTD.js"),__vite__mapDeps([21,1])),meta:{d:1730281005e3,c:["UniMelb","24S2"],g:["Quantum Computing","Linear Algebra","Complex Numbers"],e:`
      What is Observables？
      
+
      This course is an introduction to spatial data management for engineering students. The course covers the following topics:
      `,t:"GEOM90008 Spatial Data Management",s:"GEOM90008",i:"book-open",O:1,y:"a"}}],["/master/",{loader:()=>U(()=>import("./index.html-BNh3BPp9.js"),__vite__mapDeps([17,1])),meta:{d:1722404468e3,c:["UniMelb"],e:`
      Dale took his Master's degree at UniMelb. Here are some thoughts and notes.
      
+`,t:"Master",i:"book",y:"a"}}],["/other/",{loader:()=>U(()=>import("./index.html-BP3Q9Lwx.js"),__vite__mapDeps([18,1])),meta:{d:172864891e4,e:`
      Dale randomly leaves some notes here.
      
+`,t:"Other",i:"comment",y:"a"}}],["/other/hackathon.html",{loader:()=>U(()=>import("./hackathon.html-XvarX4MQ.js"),__vite__mapDeps([19,1])),meta:{d:1728813271e3,c:["Hackathon"],g:["Event"],e:`
      What is the solution?
      
+
        
+
      • Greenwashing AI fact-check agent
        • 
+
      
+
      What is the topic of the project?
      
+
        
+
      • Sustainable decision making for consumers and investors
        • 
+
      
+
      What is the specific domain of the project?
      
+
        
+
      • Greenwashing
        • 
+
      
+
      What is the background of the project?
      `,t:"A summary of my first attempt at a hackathon",s:"Hackathon",i:"laptop",y:"a"}}],["/master/COMP90084/",{loader:()=>U(()=>import("./index.html-DRROiJ7q.js"),__vite__mapDeps([20,1])),meta:{d:173948885e4,c:["UniMelb"],e:`
      Here are some notes for COMP90084.
      
+`,t:"COMP90084",i:"bookmark",y:"a"}}],["/master/COMP90084/quantum.html",{loader:()=>U(()=>import("./quantum.html-CtrHgxt8.js"),__vite__mapDeps([21,1])),meta:{d:1730281005e3,c:["UniMelb","24S2"],g:["Quantum Computing","Linear Algebra","Complex Numbers"],e:`
      What is Observables？
      
 
      (Page 125 of the textbook)
      
 
        
 
      • Observables are represented by hermitian operators. The result of an observation is always an eigenvalue of the hermitian.
        • 
 
      • The expression  represents the expected value of observing on .
        • 
 
      • Observables in general do not commute. This means that the order of observation matters. Moreover, if the commutator of two observables is not zero, there is an intrinsic limit to our capability of measuring their values simultaneously.
        • 
-
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      Resource on DL/AI (Maybe watch/listen the seminars/podcasts first, then the online courses and books)
      
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      Resource on DL/AI (Maybe watch/listen the seminars/podcasts first, then the online courses and books)
      
 
        
 
      • 
 
        Books & Online Course Materials:
        
@@ -450,12 +450,12 @@ var wf=Object.defineProperty;var _f=(e,t,n)=>t in e?wf(e,t,{enumerable:!0,config
 
      • 
 
        …
        
 
        • 
-
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      Shor算法是一种量子算法，用于分解大整数为素数的乘积。这是一个量子计算课程辅导员发布的英文笔记的翻译版本。
      
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+`,t:"Shor算法，一种用于分解大整数为素数乘积的量子算法",s:"Shor算法",i:"lightbulb",y:"a"}}],["/master/topics/",{loader:()=>U(()=>import("./index.html-YEC863qj.js"),__vite__mapDeps([24,1])),meta:{d:173948885e4,c:["UniMelb"],e:`
      Here are some candidate research topics for second-year in UniMelb.
      
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      Although deep neural networks have been widely adopted and proven effective across many applications, the backpropagation process remains complex and not easily interpretable in a way that mirrors human understanding. Humans are capable of swiftly grasping and interpreting complex patterns to make decisions, whereas neural networks depend on computationally intensive methods for learning.
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      The XV Scanner™ is the world’s only fully dedicated X-ray lung function scanner, designed to do something completely unique–capture a single, full-breath cycle in motion, from four different angles simultaneously.
      
+
      To optimise the scanning procedure, it is desirable to have a real-time, non-contact method for monitoring the patient’s breathing. This project is focused on the development of a depth-camera based breath tracking method/algorithm to extract a highly dependable, real-time breath trace from a seated patient. As the breath trace may be used for automatically initiating and terminating X-ray exposures during the patient’s breathing cycle.
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      Museum collections are an important source of information in understanding the historical knowledge on culture, people and scientific discoveries. Museums host range of data including metadata (including descriptive texts), and images of objects (from multiple views) and notes. For this project, we use the collections from the University of Melbourne Medical museums (Medical History Museum, Henry Forman Atkinson Dental Museum, Harry Brookes Allen Museum of Anatomy and Pathology).
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      With the growing adoption of wearable and mobile devices, the use of AI in mobile health is becoming increasingly relevant for a variety of healthcare applications. However, deploying AI models on these resource-limited client devices poses significant challenges, restricting their applicability. This is especially the case with the introduction of large language models (LLMs) and multimodal LLMs, which require substantial memory and therefore present additional deployment hurdles.
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      Model reprogramming [1] or input visual prompts [2] has been successfully used in reusing pretrained image classifiers. This project aims to explore whether model reprogramming can enable the transferability of pretrained deep neural networks across low-level image processing tasks. Specifically, we will investigate the feasibility of using pretrained models (e.g., U-Net) for image denoising, enhancement, and super-resolution on different tasks by introducing task-specific visual prompts.
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