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

Introduction to Machine Learning

Pierre Geurts and Louis Wehenkel
Université de Liège, Institut Montefiore


First lecture: Wednesday, September 25, 2019, 9:00AM (Room DOMAT, Building B31)
Second lecture: Wednesday, October 2, 2019, 9:00AM (Room R30, Building B6d)

Information :
List of questions for the oral exam in January 2018

Information about practicals and project for 2019-2020

Podcast Oct 9/2019 - part1
Podcast Oct 9/2019 - part2
Podcast Oct 23/2019 - part1
Podcast Oct 23/2019 - part2
Podcast Oct 23/2019 - part3

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

Lecture 1: Introduction to the course (PG)

Lectures 1 and 2: Introduction to Machine Learning (PG, update 2017) Further information (Kevin Murphy)

Lecture 2: classification and regression trees: general principles (PG).

Lecture 2: Supplements - Classification and regression trees: scores, quality measures, etc. (PG)

Lecture 2: Supplements - Classification and regression trees: about pruning (LW)

Lecture 3: Linear regression (PG)

Lecture 4: Nearest neighbor methods (LW)

Lecture 5: Artificial neural networks (LW) NEW: Video: Information Theory of Deep Learning, by Naftali Tishby

Lecture 6: Bias/variance tradeoff, model assessment and selection (PG)

Lecture 7: Support vector machines and kernel-based methods (PG)

Lecture 8: Ensemble methods and Feature selection (PG)

Lecture 9: Unsupervised learning (clustering and PCA) (PG)

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Last update: 21/12/2018