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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 Electro-mechanical Engineering
> Master [120] in Mathematical Engineering
> Master [120] in Electrical Engineering
> Master [120] in Computer Science and Engineering
> Master [120] in Biomedical Engineering
> Certificat universitaire en statistique
> Master [120] in Statistics: General
> Master [120] in Computer Science
Faculty or entity
in charge
> ELEC


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