Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)

Morten Hjorth-Jensen [1, 2]

[1] Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA
[2] Department of Physics (office FV308), University of Oslo, Norway

The teaching material is produced in various formats for running codes (jupyter notebooks) and on-screen reading. Below you will also find a link to the lecture notes as a textbook in PDF format and as a jupyter notebook as well. Projects and exercise sets are also included.

Week 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression

Week 35 August 24-28: Linear regression and review of statistics and probability theory

Week 36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression

Week 37 September 7-11: Ridge and Lasso Regression

Week 38 September 14-18: Summary of linear regression methods and start Logistic Regression

Week 39 September 21-25: Logistic Regression and Gradient methods

Week 40 September 28 - October 2: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model

Week 41 October 5-9: Building a multi-layer perceptron code and introduction to Tensorflow

Week 42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks

Week 43 October 19-23: Dimesionality Reduction, Principal Component analysis

Week 44 October 26-30: Decision Trees and Bagging

Week 45 November 2-6: Random Forests and Gradient Boosting

Week 46 November 9-13: Support Vector Machines

Week 47 November 16-20: Support Vector Machines

Week 48 November 23-27: Unsupervised learning, clustering and summary of course

Textbook

Projects and Exercises Fall 2020

First homework set, week 35

Second homework set, week 36

Project 1, Deadline October 5 (available September 1)

Project 2, Deadline November 2 (available September 28)

Project 3, Deadline December 7 (available November 2)