Overview of course material: Data Analysis and Machine Learning

Morten Hjorth-Jensen [1, 2]
[1] Department of Physics and Facility for Rare Isotope 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.

Session 1: Introduction to Data Analysis and Machine Learning

Session 2: Linear Regression, from ordinary Least Squares to Ridge and Lasso Regression

Session 3: Linear Regression, Ridge and Lasso Regression

Session 4: Resampling methods and Bias-Variance Tradeoff

Session 5: Logistic Regression and Optimization

Session 6: Deep learning, neural networks, the basics, back propagation and building our own code

Session 7: Deep learning, convolutional neural networks and recurrent neural networks

Session 8: Deep Learning, solving differential equations with neural networks

Session 9: Decision trees and ensemble methods, from random forests to boosting methods

Session 10: Support vector machines

Projects and Exercises

First exercise set (Session 1 and Session 2)

Second exercise set (Session 3)

Third exercise set (Sessions 4 and 5)

Project 1, deadline January 31

Project 2, deadline February 15