Machine Learning : regression, dimensionality reduction and data visualization

LELEC2870  2018-2019  Louvain-la-Neuve

Machine Learning : regression, dimensionality reduction and data visualization
5.0 credits
30.0 h + 30.0 h
1q

Teacher(s)
Lee John (compensates Verleysen Michel) ; Verleysen Michel ;
Language
Anglais
Main themes

Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.

Aims

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.

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.

Content
  • Linear regression
  • Nonlinear regression with multi-layer perceptrons
  • Clustering and vector quantization
  • Nonlinear regression with radial-basis function networks
  • Probabilistic regression
  • Ensemble models
  • Model selection
  • Principal Component Analysis
  • Nonlinear dimensionality reduction and data visualization
  • Independent Component Analysis
  • Kernel methods
Teaching methods

Lectures, exercises, practical sessions on computers, project to be carried out individually of by groups of 2 students

Evaluation methods

Closed book oral examination, or written examination (depending on the number of students)

Bibliography

Reference books (not mandatory) mentioned on the website of the course

Faculty or entity


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

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in data Science: Statistic
5
-

Master [120] in Chemistry and Bioindustries
5
-

Master [120] in Agricultural Bioengineering
5
-

Master [120] in Environmental Bioengineering
5
-

Master [120] in Forests and Natural Areas Engineering
5
-

Master [120] in Electrical Engineering
5
-

Master [120] in Computer Science and Engineering
5
-

Master [120] in Computer Science
5
-

Master [120] in Mathematical Engineering
5
-

Master [120] in Data Science Engineering
5
-

Master [120] in data Science: Information technology
5
-

Master [120] in Biomedical Engineering
5
-

Master [120] in Statistic: General
5
-