COS 575 - Machine Learning

Machine Learning is the study of how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, linear algebra, and probability theory, among others. The course will explain how to build systems that learn and adapt using examples from real world applications. The class will require background knowledge in linear algebra (LA) and probability (i.e. I will assume previous knowledge of LA and Statistics courses as prerequisites) a review session linear algebra and probability will precede those chapters in need of reminding background knowledge. Main topics include supervised learning including classification and regression, neural networks, decision trees and random forest, support vector machines, unsupervised learning like clustering and GMM deep convolutional neural network, reinforcement learning, etc.

Prerequisites & Notes
MAT 126, MAT 127, MAT 262, and STS 232 or STS 434 or STS 332 or STS 435 or permission by the instructor.

This course is cross listed with COS 475. Note that COS 475 and COS 575 cannot both be taken for degree credit.

Credits: 3