Machine Learning :classification and evaluation

lingi2262  2018-2019  Louvain-la-Neuve

Machine Learning :classification and evaluation
5 credits
30.0 h + 30.0 h
Dupont Pierre;
Main themes
  • Learning as search, inductive bias
  • Combinations of decisions
  • Loss function minimization, gradient descent
  • Performance assessment
  • Instance-based learning
  • Probabilistic learning
  • Unsupervised classification

At the end of this learning unit, the student is able to :


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:

  • INFO1.1-3
  • INFO2.3-4
  • INFO5.3-5
  • INFO6.1, INFO6.4

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:

  • SINF1.M4
  • SINF2.3-4
  • SINF5.3-5
  • SINF6.1, SINF6.4
Students completing successfully this course will be able to:
  • understand and apply standard techniques to build computer programs that automatically improve with experience, especially for classification problems
  • assess the quality of a learned model for a given task
  • assess the relative performance of several learning algorithms
  • justify the use of a particular learning algorithm given the nature of the data, the learning problem and a relevant performance measure
  • use, adapt and extend learning software
Students will have developed skills and operational methodology. In particular, they have developed their ability to:
  • use the technical documentation to make efficient use of existing packages,
  • communicate test results in a short report using graphics.

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”.
  • Decision Tree Learning: ID3, C4.5, CART, Random Forests
  • Linear Discriminants: Perceptrons, Gradient-Descent and Least-Square 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
  • Instance-based learning: k-NN, LVQ
  • Clustering Techniques
Teaching methods
Evaluation methods
The practical projects are worth 20 % of the final grade, 80 % for the final exam (closed book)
Projects cannot be re-implemented for the second session.
Hence, the project grade is fixed at the end of the semester.
Other information
- LSINF1121 Algorithmics and data structures - LFSAB1105 Probability and statistics:
Slides obligatoires, disponibles sur  et plus généralement tous les documents disponibles à partir du même site.
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
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