• Pre MA-222

  Introduction

  In many fields (e.g., computer vision, speech recognition, data mining, and bioinformatics), machine learning has become a crucial ingredient in translating research into applications. The course is intended to provide an in-depth overview of recent advances in machine learning, with applications in fields such as computer vision, data mining, natural language processing. Fundamental topics that will be covered include supervised (Bayesian) and unsupervised learning, non-parametric methods, graphical models (Bayes Nets and Markov Random Fields) and dimensionality reduction. The course will also cover several of the most important recent developments in learning algorithms, including boosting, Support Vector Machines and kernel methods, and outline the fundamental concepts behind these approaches.

Teacher

Philippos Mordohai