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 22-26:Basic introduction to the course with schedule etc and start Linear Regression

Week 35 August 29- September 2: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics

Week 36 September 5-9: Statistical analysis and discussion of Ridge and Lasso regression

Week 37 September 12-16: Resampling techniques, Cross-validation and the Bootstrap

Week 38 September 19-23: Summary of linear regression methods and start Logistic Regression

Week 39 September 26-30: Logistic Regression and Gradient methods

Week 40 October 3-7: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

Week 41 October 10-14: Building a multi-layer perceptron code and introduction to Tensorflow

Week 42 October 17-21: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

Week 43 October 24-28: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks

Week 44 October 31- November 4: Decision Trees and Ensemble models

Week 45 November 7-11: Decision Trees, Random Forests and Gradient Boosting

Week 46 November 14-18: Support Vector Machines

Week 47 November 21-25: Support Vector Machines and unsupervised learning and Summary of Course with Future Perspectives

Textbook

Projects Fall 2022 (dates are tentative)

Project 1, Deadline October 11 (available September 5)

Project 2, Deadline November 18 (available October 7)

Project 3, Deadline December 18 (available November 13)