CS 6830 - Machine Learning

Machine Learning is concerned with the design and analysis of algorithms that enable computers to automatically find patterns in the data. This introductory course will give an overview of the main concepts, techniques and algorithms that are relevant for the theory and practice of machine learning. The course will cover the fundamental topics of classification, regression and clustering, starting with simple learning models such as perceptrons, decision trees and logistic regression, and ending with more advanced models including Support Vector Machines, Conditional Random Fields and Bayesian networks. The description of the formal properties of the algorithms will be supplemented with motivating applications in a wide range of areas including natural language processing, computer vision, bioinformatics and music analysis.

Requisites

Credit Hours: 3.0
Repeat/Retake Information: May not be retaken.

Lecture/Lab Hours: 3.0 lecture

Eligible grades: A-F,WP,WF,FN,FS,AU,I