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 printing and on-screen reading.

Warning.

The PDF files are based on LaTeX and have seldom technical failures that cannot be easily corrected. The HTML-based files, called "HTML" and "ipynb" below, apply MathJax for rendering LaTeX formulas and sometimes this technology gives rise to unexpected failures (e.g., incorrect rendering in a web page despite correct LaTeX syntax in the formula). Consult the corresponding PDF files if you find missing or incorrectly rendered formulas in HTML or ipython notebook files.

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. Start Neural Networks

Week 40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model

Week 41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)

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: Support Vector Machines

Week 47 November 16-20: Support Vector Machines

Week 48 November 23-27: Unsupervised learning, clustering and summary of course

Projects and Exercises Fall 2020

First homework set, week 35

Second homework set, week 36

Textbook

Project 1, Deadline October 5 (available September 1)

Project 2, Deadline November 2 (available September 28)

Project 3, Deadline December 7 (available November 2)