Teaching / ML1

Machine Learning 1

A first course on probabilistic machine learning: from Bayesian foundations to neural networks, kernel methods, and ensemble models.

Below you will find the lecture notes and videos, organized per topic. A few topics also have a short interactive explainer page, linked from the topic it belongs to.

* Lecture notes are being added progressively. Videos and slides are available for all lectures now.

Week 1: Introduction & Probability Theory

Week 2: Linear Regression & Bayesian Inference

Week 3: Model Comparison & Classification

Week 4: Logistic Regression & Neural Networks

Week 5: Unsupervised Learning & Dimensionality Reduction

Week 6: Kernel Methods & Gaussian Processes

Week 7: Ensemble Methods