Machine learning : regression, deep networks and dimensionality reduction

lelec2870  2023-2024  Louvain-la-Neuve

Machine learning : regression, deep networks and dimensionality reduction
5.00 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John; Lee John (compensates Verleysen Michel); Verleysen Michel;
Language
Main themes
Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.
Learning outcomes

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

1 With respect to the AA referring system defined for the Master in Electrical Engineering, the course contributes to the develoopment, mastery and assessment of the following skills :
  • AA1.1, AA1.2, AA1.3
  • AA3.1, AA3.2, AA3.3
  • AA4.1, AA4.2, AA4.4
  • AA5.1, AA5.2, AA5.3, AA5.5
  • AA6.3
At the end of the course, students will be able to :
- Understand and apply machine learning techniques for data and signal analysis, in particular for regression and prediction tasks.
- Understand and apply linear and nonlinear data visualization techniques.
- Evaluate the performances of these methods with appropriate techniques.
- Choose between existing methods on the basis of the nature of data and signals to be analyzed.
 
Content
  • Linear regression
  • Nonlinear regression with multi-layer perceptrons (MLP)
  • Deep learning (convolutional CNN and adversarial GAN)
  • Clustering and vector quantization
  • Nonlinear regression with radial-basis function networks (RBFN)
  • Model selection
  • Feature selection
  • Principal Component Analysis (PCA)
  • Nonlinear dimensionality reduction and data visualization
  • Independent Component Analysis (ICA)
  • Kernel methods (SVM)
Teaching methods
Ex-cathedra course organized physically if sanitary conditions permit, and broadcasted or recorded if required by sanitary rules.  Practical sessions on computers, and project to be carried out individually or by groups of 2 students.
Evaluation methods
The assessment consists of two parts.
1) An assignment (course project) to be completed during the semester, and handed in as a report including answers to the questions that come with the assignment wording;
2) An oral or written examination on the course and practical sessions.
Part 1) counts for 50% of the final assessment points, part 2) for 50%.
Students who have taken the examination in the January session may, on request, retain their points from part 1) for a possible examination in the August session.  
Bibliography
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
Teaching materials
  • slides disponibles sur Moodle - slides available on Moodle
Faculty or entity
ELEC


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic

Master [120] in Biomedical Engineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Linguistics

Master [120] in Environmental Bioengineering

Master [120] in Electrical Engineering

Master [120] in Statistics: General

Master [120] in Chemistry and Bioindustries

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Certificat d'université : Statistique et science des données (15/30 crédits)

Master [120] in Agricultural Bioengineering

Master [120] in Data Science: Information Technology

Master [120] in Energy Engineering