Machine Learning aims at transforming raw data into predictive
  models and understandings

Introduction to Machine Learning

Pierre Geurts and Louis Wehenkel
Université de Liège, Institut Montefiore


Starting from October 21, 2020 (and until further notice), the lectures will be exclusively given online from 9:00AM to 11:00AM, using LifeSize (link to the virtual classroom below).

Information :
List of questions for the oral exam in January 2020-2021

Information about practicals and project for 2020-2021

Follow our 2020 IML Lectures in real-time on Lifesize: NB: The room was changed on October 21.

Video recordings of past lectures:

Slides and other useful documents:
Web page of the 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 reference book (Machine Learning: a Probabilistic Perspective 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

Lecture 9: Unsupervised learning, Clustering and PCA

Last update: 21/10/2020