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 35 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression

Week 36 August 30- September 3: Linear regression and review of statistics and probability theory

Week 37 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression

Week 38 September 13-17: Ridge and Lasso Regression

Week 39 September 20-24: Summary of linear regression methods and start Logistic Regression

Week 40 September 27- October 1: Logistic Regression and Gradient methods

Week 41 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model

Week 42 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow

Week 43 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks

Week 44 October 25-29: Dimesionality Reduction, Principal Component analysis

Week 45 November 1-5: Decision Trees and Bagging

Week 46 November 8-12: Random Forests and Gradient Boosting

Week 47 November 15-19: Gradient boosting and Support Vector Machines

Week 48 November 22-26: Support Vector Machines and Workshop on Project 3

Week 49 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives

Textbook

Projects and Exercises Fall 2021, to be updated

First homework set, week 35

Second homework set, week 36

Project 1, Deadline October 10 (available September 1)

Project 2, Deadline November 13 (available October 6)

Project 3, Deadline December 16 (available November 9)