Machine learning seminar
[INGI2379]
[30h] 3 credits
This course is taught in the 2d semester.
Teacher(s): Pierre Dupont(coord.), Michel Verleysen
Language: English
Level: Second
Aims
- To summarize a technical or scientific paper of the domain, convey it to colleagues, and discuss it with a critical viewpoint
Main themes
Content and teaching methods
- 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
Teaching method: student seminars
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)
Prerequisites: having passed at least one of the following courses:
- INGI2262 Machine Learning
- ELEC2870 Artificial neural networks
- SINF2275 Data mining and decision making