Overview of course material: Data Analysis and Machine Learning

Morten Hjorth-Jensen [1, 2]

[1] Department of Physics and Facility for Rare Ion Beams, Michigan State University, USA
[2] Department of Physics, University of Oslo, Norway

The teaching material is produced in various formats for printing and on-screen reading.


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.

Day 1: Introduction to Data Analysis and Machine Learning and Linear Regression

Day 2: Linear Regression and Bias-Variance Tradeoff

Day 3: Logistic Regression and Optimization

Day 4: Neural Networks and Deep Learning

Day 5: Decision trees, Random Forests and Boosting