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 19-23:Basic introduction to the course with schedule etc and start Linear Regression

Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics

Week 36 September 2-6: Statistical analysis and discussion of Ridge and Lasso regression

Week 37 September 9-13: Resampling techniques, Cross-validation and the Bootstrap

Week 38 September 16-20: Summary of linear regression methods and start Logistic Regression

Week 39 September 23-27: Logistic Regression and Gradient methods

Week 40 October 2-6: Stochastic Gradient Descent

Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow

Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

Week 44 October 28- November 1: Convolutional Neural Networks

Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks

Week 46 November 11-15: Decision trees, random forests and other ensemble methods

Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives

Textbook

Projects Fall 2023 (dates are tentative)

Project 1, Deadline October 15 (available September 3)

Project 2, Deadline November 13 (available October 8)

Project 3, Deadline December 11 (available November 12)