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 and Center for Computing in Science Education (office FØ470), 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 23-27:Basic introduction to the course with schedule etc and start Linear Regression

Week 35 August 30- September 3: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression

Week 36 September 6-10: Statistical analysis and discussion of Ridge and Lasso regression

Week 37 September 13-17: Resampling techniques, Cross-validation and the Bootstrap

Week 38 September 20-24: Summary of linear regression methods and start Logistic Regression

Week 39 September 27- October 1: Logistic Regression and Gradient methods

Week 40 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

Week 41 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow

Week 42 October 18-22: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks

Week 44 November 1-5: Decision Trees and Bagging

Week 45 November 8-12: Random Forests and Gradient Boosting and Workshop on Project 3

Week 46 November 15-19: Gradient boosting and Support Vector Machines

Week 47 November 22-26: Support Vector Machines

Week 48 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives

Textbook

Projects Fall 2021 (dates are tentative)

Project 1, Deadline October 11 (available September 10)

Project 2, Deadline November 15 (available October 12)

Project 3, Deadline December 13 (available November 12)