Machine Learning | Columbia University COMS 4771

Comprehensive course on machine learning techniques, including generative and discriminative models, taught by an expert professor with hands-on MATLAB implementation.

Introduction

This course introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods.

Highlights

• Covers a wide range of machine learning topics, including both generative and discriminative models
• Taught by Professor Tony Jebara, an expert in the field of machine learning
• Hands-on implementation of several algorithms in MATLAB
• Prerequisites include background in linear algebra, statistics, and mathematical maturity

Recommendation

This course is recommended for students who have a strong foundation in linear algebra and statistics, and are interested in gaining a comprehensive understanding of machine learning techniques. The course is suitable for both beginners and advanced learners in the field of machine learning.