Overview of course material: Data Analysis and Machine Learning

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, 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.

Introduction to Data Analysis and Machine Learning

Getting started with Machine Learning

Review of central linear algebra elements

Monte Carlo methods and elements of probability theory

Regression Methods

Gradient methods

Logistic Regression

Neural Networks

Reduction of dimensionality

Decision trees, from simple to random ones

Support Vector Machines

Convolutional Neural Networks

Projects and Exercises

First exercise set (Day 2)

Second exercise set (Day 2 and 3)

Third exercise set (Days 4 and 5)

Fourth exercise set (Days 7-9)