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Number of hours
Lectures : 18.0
ECTS : 1.75
This course will provide an introduction to modern techniques for Bayesian pattern recognition and machine learning. We will begin with a review of linear regression, generative and discriminative methods for linear classification and the use of Kernel Methods and Support Vector Machines. We will then examine Bayesian Networks, Gaussian Mixture Models and principal components analysis.
The course will provide basic competence in machine learning methods that can be used to construct systems for data mining, communications, signal analysis, computer vision, speech recognition, man-machine interaction, and intelligent systems.
This course will be taught in French using an english language text book.
Contact Jean-Baptiste DURAND
The course will closely follow the development of the text book by Chris Bishops text book on Pattern Recognition and Machine Learning.
S1: Introduction, probability distributions, likelihood. S2: Bayesian Probability Theory S3: Gaussian Probability Density Functions S4: Multivariate Gaussian Probability Density Functions S5: Principal Component Analysis S6: Mixture Models and Clustering S7: Generative Methods for Classification S8: Fisher Linear Discriminant and Perceptrons S9: Perceptron and Kernel Methods S10: Neural Networks S11: Support Vector Machines S12: Combining Models, Boosting.
Linear Algebra, Multivariate Calculus Optimization Probability and Statistics.
Both Session 1 Exam and the make-up exam will be 3 hour written exams with documents authorised.