MAP566A - Introduction to Machine Learning (2023-2024)

The lecture will mostly be based on the book "Probabilistic Machine Learning: An Introduction" by Kevin Murphy. Lecture notes are provided. The following topics will be discussed:
- Introduction to machine learning: Motivations, elements of statistical learning, K-nearest neighbors as a first learning method
- Least square regression: Simple regression, regularization to avoid overfitting (Ridge and LASSO)
- Classification with logistic regression
- Stochastic gradient methods
- Principal component analysis
- Support vector machines, including an introduction to kernel methods
- Trees and ensemble methods, including boosting
- Neural networks
- Clustering methods