DATA 602 - Introduction to Data Analysis and Machine Learning

Credits: [3]

Description: This course provides a broad introduction to the practical side of machine-learning and data analysis. This course examines the end-to-end processing pipeline for extracting and identifying useful features that best represent data, a few of the most important machine algorithms, and evaluating their performance for modeling data. Topics covered include decision trees, logistic regression, linear discriminant analysis, linear and non-linear regression, basic functions, support vector machines, neural networks, Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design.
Course ID: 102549
Prerequisite: DATA 601
Components: Lecture
Grading Method: A-F