Machine Learning

Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).

5 credits

30.0 h

Teacher(s)

Lebichot Bertrand;

Language

English

Prerequisites

MQANT1227 Mathématiques de gestion 2
MQANT1221 Inférence statistique
MINFO1201 Informatique et algorithmique

(or equivalent)

Main themes

The main themes for this course are the following:

Dimensionality reduction methods:Principal Component Analysis, Singular Value Decompositionand Multidimensional Scaling
Kernel methods for classification and regression
Bayesian networks and graphical models
Markov models and hidden Markov model
Reinforcement Learning

Those themes are complementary to those presented in MLSMM2151 'Data Mining'.

Aims

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

With respect to the LSM competency framework. This course contribute to acquiring the following competencies:
Knowledge and reasoning

Mastery of the core knowledge for each area of management.
Ability to communicate one's acquired knowledge from the various areas of management.
Ability to properly apply one's acquired knowledge in order to solve problems.

A scientific and systematic approach

Clear, structured, analytical reasoning based on applying, and if needed adapting, scientifically-based conceptual frameworks and models to define and analyse a problem.
Collecting, selecting and analysing relevant information using rigorous, advanced and appropriate methods.

At the end of this course, the student will be able to:

Understand and describe the main methods used in Machine Learning.
Apply dimensionality reduction techniques, when required.
Determine the most relevant methods to use for a given learning problem.
Apply those methods on real-life learning problems.

Content

Nowadays, the volume of data generated, for instance by internet and social networks, is constantly increasing. On the other hand, there is a great need for efficient ways to infer useful information from those data, which can take different forms. Numerous data mining, machine learning and pattern recognition algorithms were developed in order to predict information for different applications. This course is devoted to some of those techniques, emphasizing on dimensionality reduction, Kernel and Bayesian models and some graph related methods.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Lectures
Practical sessions integrated to those lectures
A project based on lectures and practical sessions

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The final mark takes two results into account:

The project evaluation
In session, an oral examination

Other information

This course has strong technical requirements :
- In mathematics : matrix computation, linear algebra, optimization
- In statistics : multivariate statistics and statistical inference
- In computer science : programmation (like R, Python, and Matlab), algorithmic

Online resources

Course notes are available on https://moodleucl.uclouvain.be/

Bibliography

Recommended books :
BISHOP C., Pattern Recognition and Machine Learning, Springer, 2006.
DUDA R., Patter Classification (second edition), Wiley, 2001.
ALPAYDIN E., Introduction to Machine Learning, 2nd Ed., The MIT Press, 2009.
THEODORIDIS S., Machine Learning : A Bayesian and Optimization Perspective, Academic Press, 2015.
SUTTON R., Reinforcement Learning : An introduction, The MIT Press, 1998.

Faculty or entity

CLSM

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

Title of the programme

Sigle

Credits

Prerequisites

Aims

Master [120] : Business Engineering

INGM2M

Master [120] : Business Engineering

INGE2M