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Introduction to statistical learning theory and kernel-based methods.
Applications in bioinformatics, computer vision, text mining, audio processing, etc.
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
This course is given in english only