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: Gradient boosting and Support Vector Machines

Week 47 November 16-20: Support Vector Machines and Workshop on Project 3

Week 48 November 23-27: Support Vector Machines and Summary of Course with Future Perspectives

Textbook

Projects and Exercises Fall 2020

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)