Code
IGFE MAT 7008
Level
MSc
Graduate
PostGraduate
Semester
Fall
Domain
Mathématiques
Program
Master of Science
Language
Anglais/English
ECTS Credits
3
Class hours
30
Workload
75
Program Manager(s)
Department
- Electronique et Physique
Educational team
Organisation
Cours/TD/TP/projet/examen :Learning objectives
Emphasize and apply the concepts studied in Probability and Statistics to fundamentals engineering problems such as array processing, data science, pattern recognition, etc.
Prerequisites
Probability and statistics (Core courses S1), Linear Algebra
Keywords
Statistical Data Analysis, decision theory, Unsupervised Methods, Supervised Methods
Content
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.