This repository contains all learning material pertinent to the Machine Learning course at FRIB/NSCL@MSU, from May 18, 2020.
Here you will find a general overview of the course, with teaching schedule and link to material
We plan to have lectures MTWTF (all days) from 10am to 12pm, with breaks and question sessions, and possible exercise and further question and answer sessions 2pm-3pm. For those of you not too familiar with Python as programming language, we may decide upon more dedicated sessions. During the lectures we will use a mix of whiteboard writing (like a traditional blackboard lecture) and the jupyter-notebooks which you find by scrolling down the presentation link at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/web/course.html.
The lectures will be recorded and the videos will be made available right afterwards, together with the hand-written notes. These will be added as links below here. Solutions to exercises will also be added in due time. There is no exercise session the last Friday of the course.
We will assume you have some basic knowledge of linear algebra as well, and some elementary concepts from statistics like mean values, variance, covariance and some basic distributions like the Uniform distribution, Gaussian and Exponential distributions. There is some material on this at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Statistics/html/Statistics.html and https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Linalg/html/Linalg.html
- Monday: Introduction to Machine Learning, with an emphasis on Linear Regression, see the introductory slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/How2ReadData/html/How2ReadData-bs.html and the regression slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Regression/html/Regression.html Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay18.pdf and video at https://mediaspace.msu.edu/media/t/1_qz4id3p6
- Tuesday: Discussion of linear regression examples, hands-on exercises. The material is covered by the slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Regression/html/Regression.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay19.pdf and video at https://mediaspace.msu.edu/media/t/1_c06sswf0
- Wednesday: Logistic regression and classification problems, introducing gradient descent. See slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/LogReg/html/LogReg.html and https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Splines/html/Splines.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay20.pdf and video at https://mediaspace.msu.edu/media/t/1_shosk7c1
- Thursday: More on optimization and gradient descent. Slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/Splines/html/Splines.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay21.pdf and video at https://mediaspace.msu.edu/media/t/1_u69zibvz
- Friday: Continuation of gradient descent and begin Decision trees, Random forests, bagging and boosting, material at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/DecisionTrees/html/DecisionTrees.html. Video at https://mediaspace.msu.edu/media/t/1_o6uox0ch. Video of exercise session at https://mediaspace.msu.edu/media/t/1_c1suzeeq
Solution to exercises for the first week will be available over the weekend.
- Monday: Decision trees, Random forests, bagging and boosting, material at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/DecisionTrees/html/DecisionTrees.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay25.pdf and video at https://mediaspace.msu.edu/media/t/1_a0wrdrbz
- Tuesday: Neural networks, basics, see material at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/NeuralNet/html/NeuralNet.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay26.pdf and video at https://mediaspace.msu.edu/media/t/1_7nm3kegw
- Wednesday: Neural networks, essential elements and making your own code. See slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/NeuralNet/html/NeuralNet.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay26.pdf and video at https://mediaspace.msu.edu/media/t/1_o04m014g
- Thursday: More Neural networks and deep learning. Slides at https://mhjensen.github.io/MachineLearningMSU-FRIB2020/doc/pub/NeuralNet/html/NeuralNet.html. Links to handwritten notes at https://github.com/mhjensen/MachineLearningMSU-FRIB2020/blob/master/doc/HandWrittenNotes/NotesMay28.pdf and video at https://mediaspace.msu.edu/media/t/1_rlael9c1
- Friday: Discussion of experimental nuclear physics applications of deep learning methods, Michelle Kuchera (Davidson College). Link with Michelle's lecture at https://mediaspace.msu.edu/media/t/1_34k2cifh No exercise session the last day of the course.
Feel free to send me or Michelle Kuchera an email about ML related topics and more.