Machine Learning : regression, dimensionality reduction and data visualization [ LELEC2870 ]
5.0 crédits ECTS
30.0 h + 30.0 h
1q
| Teacher(s) |
Verleysen Michel ;
|
| Language |
English
|
Place
of the course |
Louvain-la-Neuve
|
| Main themes |
See summary.
|
| Aims |
1. To understand and to be able to apply machine learning concepts for analyzing data and signals, in particular in the context of regression and prediction problems;
2. To understand and to be able to apply linear and nonlinear techniques for data visualization;
3. To be able to evaluate the performances of these methods through appropriate techniques;
4. To be able to choose between existing machine learning techniques, according to the nature of the data and signals to be analyzed.
|
| Content |
" Linear regression
" Nonlinear regression with Multi-Layer-Perceptrons
" Clustering and vector quantization
" Nonlinear regression with Radial-Basis Function Networks, Kernel regression
" Probabilistic models for Regression
" Ensemble models
" Feature selection
" Model selection
" Principal Component Analysis
" Nonlinear dimensionality reduction and data visualization
" Independent Component Analysis
|
| Other information |
The course necessitates only a basic knowledge in linear algebra. In addition to the course itself there are exercise sessions organized on computers, and students must realize d a project aims at applying machine learning techniques in a specific application context. The exam is oral (if the number of students remains limited enough); the project report is evaluated too.
|
Cycle et année
d'étude |
> Master [120] in Statistics: General
> Certificat universitaire en statistique
> Master [120] in Computer Science and Engineering
> Master [120] in Computer Science
> Master [120] in Mathematical Engineering
> Master [120] in Electrical Engineering
> Master [120] in Biomedical Engineering
> Master [120] in Electro-mechanical Engineering
|
Faculty or entity
in charge |
> ELEC
|
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