Introduction to Machine Learning Pierre Geurts and Louis Wehenkel Université de Liège, Institut Montefiore ELEN062-1 First lecture: Thursday, September 17, 2026, 9:00AM, Room Karl MARX, Building B31, Sart-Tilman Campus.

NEW on Nov 27 2025: List of questions to prepare the oral exam of January 2026

Information about practicals and projects for 2025-2026

Slides and other useful documents:

Reference text book (The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani and Jerome Friedman)

Web page of another series of reference books (Machine Learning: a Probabilistic Perspective etc. by Kevin Patrick Murphy)

You can find below the slides used during the lectures

Introduction to the course

Lecture 1: Introduction to Machine Learning

Lecture 2: Classification and regression trees

Lecture 3: Linear regression

Lecture 4: Nearest neighbor methods

Lecture 5: Artificial neural networks

Lecture 6: Bias/variance tradeoff, Model assessment and selection

Lecture 7: Support vector machines and Kernel-based methods

Lecture 8: Ensemble methods and Feature selection (new version of November 2023)

Lecture 9: Unsupervised learning, Clustering and PCA

Video recordings of the 2020-2021 lectures:

• September 16, 2020(L.W.)
• September 23, 2020 (L.W.)
• September 30, 2020: (L.W.)
• October 7, 2020 (L.W.)
• October 14, 2020 (L.W.)
• October 21, 2020: first part, second part.
• November 6, 2020: first part, second part.
• November 18, 2020: first part, second part.(P.G.)
• November 25, 2020: first part, second part (P.G.)
• December 2, 2020: first part, second part.
• December 9, 2020: video.