5 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Dupont Pierre;
Language
English
Main themes
 Learning as search, inductive bias
 Combinations of decisions
 Loss function minimization, gradient descent
 Performance assessment
 Instancebased learning
 Probabilistic learning
 Unsupervised classification
Aims
At the end of this learning unit, the student is able to :  
1  Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Students will have developed skills and operational methodology. In particular, they have developed their ability to:

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
 Decision Tree Learning: ID3, C4.5, CART, Random Forests
 Linear Discriminants: Perceptrons, GradientDescent and LeastSquare Procedures
 Maximal Margin Hyperplanes and Support Vector Machines
 Probability and Statistics in Machine Learning
 Performance Assessment: Hypothesis testing, Comparing Learning Algorithms, ROC analysis
 Gaussian Classifiers, Fisher Linear Discriminants
 Bayesian Learning: ML, MAP, Optimal Classifier, Naive Bayes
 Instancebased learning: kNN, LVQ
 Clustering Techniques
Teaching methods
 Lectures
 Practical projects with semiautomatic feedback from the Inginious server https://inginious.info.ucl.ac.be/
 Query/answer sessions through a Moodle Forum
Evaluation methods
The practical projects are worth 20 % of the final grade, 80 % for the final exam (closed book)
Projects cannot be reimplemented for the second session.
Hence, the project grade is fixed at the end of the semester.
Projects cannot be reimplemented for the second session.
Hence, the project grade is fixed at the end of the semester.
Other information
Background:
 LSINF1121 Algorithmics and data structures https://uclouvain.be/encourslsinf1121.html  LFSAB1105 Probability and statistics: https://uclouvain.be/encoursLFSAB1105.html
 LSINF1121 Algorithmics and data structures https://uclouvain.be/encourslsinf1121.html  LFSAB1105 Probability and statistics: https://uclouvain.be/encoursLFSAB1105.html
Online resources
Bibliography
Slides obligatoires, disponibles sur http://moodleucl.uclouvain.be/course/view.php?id=8900 et plus généralement tous les documents disponibles à partir du même site.
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Computer Science and Engineering
Master [120] in Electrical Engineering
Master [120] in Statistic: Biostatistics
Master [120] in Biomedical Engineering
Master [120] in Statistic: General
Master [120] in Computer Science
Master [120] in Mathematical Engineering
Master [120] in data Science: Statistic
Master [120] in data Science: Information technology