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 (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.

Basic introduction to the course with schedule etc

Introduction to Data Analysis and Machine Learning

Getting started with Machine Learning with simple Examples

Review of central linear algebra elements

Monte Carlo methods and elements of probability theory

Regression Methods

Gradient methods and Minimization Algorithms

Logistic Regression

Neural Networks

Convolutional Neural Networks

Reduction of dimensionality

Decision Trees and Random Forests

Support Vector Machines

Unsupervised Learning, Boltzmann Machines

Recurrent Neural Networks

Autoencoders

Reinforcement Learning

Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks

Elements of Bayesian theory and Bayesian Neural Networks

Summary

Projects and Exercises Fall 2020

First homework set, week 35

Second homework set, week 36

Project 1, Deadline October 5 (available September 1)

Project 2, Deadline November 2 (available September 28)

Project 3, Deadline December 7 (available November 2)