Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h
Q2
Teacher(s)
Lebichot Bertrand;
Language
English
Prerequisites
- MQANT1227 Mathématiques de gestion 2
- MQANT1221 Inférence statistique
- MINFO1201 Informatique et algorithmique
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
Aims
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
|
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
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 (with a linked project)
- One flipped classroom
Evaluation methods
- Flipped classroom evaluation
- Project evaluation
- In session oral examination based on learning outcomes.
- 30 min preparation + 30 min oral examination
Other information
For this course has technical requirements :
- In mathematics : matrix computation, linear algebra, optimisation
- In statistics : multivariate statistics and statistical inference
- In computer science : programmation (like R, Python and Matlab), algorithmic
- In mathematics : matrix computation, linear algebra, optimisation
- 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.
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
