Lab.ML

(a repsitory that serves as my lab for exploring Machine Learning tools & algorithms)

ML News Sources

• http://www.kdnuggets.com

• https://www.reddit.com/r/MachineLearning

• Neural Information Processing Systems Conference (NIPS) Proceedings

• http://www.analyticsvidhya.com/

• http://googleresearch.blogspot.com

• http://blog.revolutionanalytics.com/

• http://www.datasciencecentral.com/

Consortiums

• http://www.partnershiponai.org/

ML Conferences

• https://spark-summit.org/

Recommended ML Books

• Deep Learning, by Ian Goodfellow, et al

  • http://www.deeplearningbook.org/
  • https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618
  • https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/

• Artificial Intelligence, A Modern Approach, Fourth Edition, Stuart Russell and Peter Norvig

  • http://aima.cs.berkeley.edu/

• Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series) 1st Edition

• Recommender Systems, by Aggaral, Charu C.

• http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

• http://www.gaussianprocess.org/gpml/chapters/

• http://www.intechopen.com/books/machine_learning

Suggested Supplemental Books

• Computer Age Statistical Inference: Algorithms, Evidence and Data Science

• Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest

• http://statweb.stanford.edu/~tibs/ElemStatLearn/

• Probabilistic Programming and Bayesian methods for Hackers

  • https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
  • https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

ML References

• http://scikit-learn.org/stable/tutorial/machine_learning_map/

• https://github.com/kailashahirwar/cheatsheets-ai

Tutorials / Online Courses

• Artificial Intelligence, MIT Open Courseware

• https://gym.openai.com/

• http://ciml.info/

• Machine Learning for Developers

  • http://linkis.com/xyclade.github.io/DyY93

• https://www.aiworkbox.com/lessons

• https://www.hackerearth.com/practice/machine-learning/

• https://www.kaggle.com/kernels

• Advanced Deep Learning & Reinforcement Learning

• AI For Medicine (3-course specialization
  • https://www.deeplearning.ai/ai-for-medicine/

YouTube Videos

• DeepLearning.ai Channel
  • For example:
    • Pie & AI: Real-world AI Applications in Medicine

University Machine Learning Labs / Programs

• MIT Computer Science and Artificial Intelligence Laboratory

• http://www.cis.upenn.edu

  • Penn Research in Machine Learning (PRiML)
    • http://priml.upenn.edu/Main/Resources
  • The Warrent Center for Network & Data Sciences
  • GRASP Lab
  • CIS 520 Machine Learning - 2015
  • CIS 521 - Artificial Intelligence Spring 2016
  • CIS 620 - Advanced Topics in AI - Spring 07 Probabilistic Graphical Models
    • http://www.seas.upenn.edu/~cis620/handouts/list.html
  • CIS 620 - Advanced Topics in AI - Spring 09 Probabilistic Graphical Models
  • CIS 700 - Advanced Topics in Machine Learning - Spring 08 Monte Carlo Methods and Nonparametric Bayesian Models

• Stanford Artificial Intelligence Laboratory

  • Probabilistic Graphical Models

• UC Berkeley amplab

• https://cs94si.wordpress.com/syllabus/

ML Software Resources

• http://mloss.org/community/

• http://nbviewer.jupyter.org/

  • http://jupyter.readthedocs.io/en/latest/index.html

• https://databricks.com/product/databricks

Interesting Github ML Resources

• Distributed (Deep) Machine Learning Community

• https://github.com/dmlc/xgboost

• https://github.com/tensorflow/tensorflow

• https://github.com/apache/incubator-singa

• Apache Singa - A General Distributed Deep Learning Platform

• https://github.com/fchollet/keras

• https://github.com/h2oai/h2o-3

Interesting Github Repository Collections:

• https://github.com/codertimo

• https://github.com/waleedka

Interesting Articles

• TO-DO: Move/Merge this section to the NEWS.md
• 2019
  • Viptul Patel, LinkedIn: The Ultimate guide to AI, Data Science & Machine Learning, Articles, Cheatsheets and Tutorials ALL in one place

• 2018

• 2017

  • [https://www.theguardian.com/science/2017/nov/01/cant-compete-universities-losing-best-ai-scientists](...universities are losing their best AI scientists)

• 2016

  • Google Deep Mind Moves to Tensor Flow
  • HAKMEM - MIT AI Laboratory Memo No 239 Feb 29, 1972
  • XGBoost: A Scalable Tree Boosting System
    • http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html
  • Detrimental Point Processes, Alex Kulesza & Ben Traskar
  • ImageNet Classification with Deep Convolutional Natural Networks

• 2015

  • Difference between Machine Learning & Statistical Modeling
  • databricks: The Unreasonable Effectiveness of Deep Learning on Spark - 2016 (move up)
  • The Hitchhiker's Guide to Data Sciene, Machine Learning, R, Python
  • 50+ Data Science and Machine Learning Cheat Sheets
  • How to Build ML with Google Prediction API

• http://blog.revolutionanalytics.com/2016/08/deep-learning-part-1.html

Interesting Researchers in ML

• Tianqi Chen
  • http://homes.cs.washington.edu/~tqchen/
• Ben Taskar (deceased, 2013)
  • http://homes.cs.washington.edu/~taskar/
• Brandon Rohr
  • http://brohrer.github.io/
  • https://twitter.com/_brohrer_

AI/ML Focused Twitter Accounts

• https://twitter.com/shivon