The lecture will mostly be based on the book "Probabilistic Machine Learning: An Introduction" by Kevin Murphy. It will cover various learning algorithms, mostly in the supervised setting (both for classification and regression), but also in some unsupervised settings. The following topics will be discussed:
- Introduction to machine learning
- Least square regression
- Classification with logistic regression
- Stochastic gradient methods
- Principal component analysis
- Support vector machines
- Trees and ensemble methods
- Neural networks
- Clustering methods
