Machine learning seminar [ LINGI2379 ]

> https://icampus.uclouvain.be/claroline/course/index.php?cid=lingi2379

Themes are chosen in the domain of machine learning

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
• INFO3.1, INFO3.2
• INFO5.3-6
• INFO6.1, INFO6.3, 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
• SINF3.1, SINF3.2
• SINF5.3-6
• SINF6.1, SINF6.3, SINF6.4

Students completing this course successfully will be able to

• study current issues in machine learning, pattern recognition or data analysis
• summarize a technical or scientific paper of the domain, convey it to colleagues, and discuss it with a critical viewpoint

The evaluation focuses on the quality of the presentations made ''by each student in front of to other participants in the seminar.
The overall grade consists of:
- 50% on the educational quality of the presentation
- 50% on the accuracy of the scientific content of the presentation

In the second session, the evaluation is 100% on a written report to the teacher the first day of the examination session.

The course is organised as a seminar where student meet regularly to present and discuss recent scientific papers.

Les séminaires pourront être présentés en anglais ou en français par les étudiants.

Illustrative examples:

• Semi-supervised learning methods
• Structured data mining (graphs, trees, sequences, etc.)
• Kernel methods for classification and regression
• Variable selection methods
• Hidden Markov models and their applications
• Boosting and bagging algorithms
• Automata induction techniques

Scientific articles in Machine Learning, supplemented by one or the other textbooks depending on the choice of students's topics.

Examples:

• Statistics for High-Dimensional Data: Methods, Theory and Applications, Bühlmann and van Geer, Springer, 2011.
• Nonlinear Dimensionality Reduction, Lee and Verleysen, Springer, 2007.
• Computational Methods of Feture Selection, Liu and Motoda, Chapman & Hall / CRC, 2008.

Background (having passed at least one of the following courses) :

• LINGI2262 Machine Learning
• LELEC2870 Artificial neural networks
• LSINF2275 Data mining and decision making