BLG 601E - Pattern Recognition

Course Objectives

This course will provide a deeper theoretical understanding of methods and issues in machine learning/pattern recognition. Students will also gain theoretical and practical experience through programming exercises and projects.

Course Description

Introduction, mathematical preliminaries; Pattern Recognition basics; Probability Distributions; Linear Models for Regression; Linear Models for Classification; Neural Networks; Kernel Methods; Sparse Kernel Machines; Graphical Models; Mixture Models and EM; Continuous Latent Variables ; Combining Models; Sequential Data; Approximate Inference; Sampling Methods