MACHINE LEARNING

Course Code:
CIS 571
Active Term:
Fall
/ All
Course Description:

This course introduces students to various machine learning techniques and tools. Topics include: supervised learning (linear and quadratic discriminant function analysis, logistics regression, kernel and k-nearest neighbor, naive Bayes, support vector machines, tree classification methods, and ensemble methods such as bagging, boosting, and random forests, unsupervised learning (k-means, hierarchical, and model-based clustering), and techniques for evaluating learning algorithms.

Credit:
3
Instruction methods:
Lecture
Total hours: 45