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


First lecture: Wednesday, September 18, 2024, 9:00AM, Room Laurent (4/89), Building B31.


Information:
List of questions to continuously prepare the oral exam of January 2024

Information about practicals and projects for 2024-2025


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:



Last update: 23/09/2024