Learning outcomes
- Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning
- Be capable of extending the acquired knowledge to other systems and cases
- Have an understanding of central algorithms used in data analysis and machine learning
- Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications
- Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression
- Learn about various neural networks and deep learning methods for supervised and unsupervised learning
- Learn about about decision trees and random forests
- Learn about support vector machines and kernel transformations
- Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
- Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++