BMI 543 Machine Learning

Credits: 3
This course provides a broad introduction to techniques for building computer systems that improve through experience. It provides both conceptual grounding and practical experience with several learning systems. The course provides grounding for advanced study in statistical learning methods, and for work with adaptive technologies used in speech and image processing, robotic planning and control, diagnostic systems, complex system modeling, and iterative optimization. Topics include: learning paradigms and concept learning, decision trees, artificial neural networks, statistical sampling and empirical error estimation, Bayesian learning (including an introduction to belief networks), clustering, principal and independent component analysis, generalization theory, memory-based (instance) techniques, evolutionary computation, and reinforcement learning. Students will gain practical experience implementing and evaluating systems applied to pattern recognition, prediction, and optimization problems.

Graded: A-F
May be taken only once for credit
Also offered as: BMI 643