Ensimag Rubrique Formation 2022

Advanced learning models - WMMS536I

  • Number of hours

    • Lectures 18.0


    ECTS 3.0


Introduction to statistical learning theory and kernel-based methods.
Applications in bioinformatics, computer vision, text mining, audio processing, etc.

Contact Julien MAIRAL, Jakob VERBEEK


I. Introduction

I.1. Statistical learning: issues and goals
I.2. Risk convexification and capacity control
I.3. Convex optimization for statistical learning
I.4 Real applications

II. Kernel-based methods

II.1. Similarity measures and reproducing kernels
II.2. Reproducing kernel Hilbert spaces
II.4. Main families of reproducing kernels
II.3. Regularization as spectral function

III. Supervised statistical learning

III.1. Kernel Ridge Regression
III.2. Kernel Logistic Regression
III.3. Support Vector Machine
III.4. Capacity control and risk bounds

IV. Unsupervised statistical learning

II.1. Kernel Principal Component Analysis
II.2. Kernel Canonical Correlation Analysis
II.3. Spectral clustering
II.4. Large margin clustering
III.4. Capacity control and risk bounds


Probability, statistics, linear algebra.


The exam is given in english only 

Additional Information

This course is given in english only EN

Curriculum->MSc MSIAM->MSIAM - Semester 3