Machine Learning

mlsmm2154  2021-2022  Mons

Machine Learning
5.00 credits
30.0 h
Q2
Teacher(s)
Saerens Marco;
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'.
Learning outcomes

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

1 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
  • Lectures
  • Practical sessions integrated to those lectures
  • A project based on lectures and practical sessions
Evaluation methods
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
Learning outcomes
Master [120] : Business Engineering
INGM2M
5
Master [120] : Business Engineering
INGE2M
5