Code
IGFE MAT 7008
Niveau
MSc
Graduate
PostGraduate
Semestre
Fall
Domaine
Mathématiques
Programme
Master of Science
Langue
Anglais/English
Crédits ECTS
3
Heures programmées
30
Charge de travail
75
Coordonnateur(s)
Département
- Electronique et Physique
Equipe pédagogique
Organisation
Cours/TD/TP/projet/examen :Acquis d'apprentissage
Emphasize and apply the concepts studied in Probability and Statistics to fundamentals engineering problems such as array processing, data science, pattern recognition, etc.
Prérequis
Probability and statistics (Core courses S1), Linear Algebra
Mots-clés
Statistical Data Analysis, decision theory, Unsupervised Methods, Supervised Methods
Contenu
I Estimation
2.1 Estimation of deterministic parameters - Bias, variance, Cramér-Rao bounds, Maximum Likelihood estimator.
2.2 Estimation of random parameters - Condiational Mean estimator, Maximum a Posteriori estimator, Bayesian Cramér-Rao bound
II Dimension Reduction
2.1 Linear Subspace Methods (Principal component analysis, Linear Discriminant Analysis)
2.2 Feature Selection
III Clustering
3.1 k-means
3.2 Hierarchical Clustering
3.3 Gaussian Mixture Models
IV Regression
4.1 Linear Regression
4.2 Multilinear Regression
4.3 Logistic regression
V Classification
5.1. Decision Trees
5.2. Random Forests
Evaluation
Grading is as follows
Written exam.