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


ELEN062-1


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