COS 475 - 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 requires a good background knowledge in linear algebra (LA) and probability. The course will explain how to build systems that learn and adapt using examples from real-world applications. Main topics covered in this course include supervised learning such as classification and regression, lasso, feature selection, neural networks, random forest, support vector machines, unsupervised learning like PCA, clustering, and GMM, deep convolutional neural network, generative adversarial networks, reinforcement learning, etc.

Prerequisites: MAT 262 (or MAT 258) and STS 232 (or STS 434 or STS 332 or STS 435)

Course Typically Offered: Spring

Credits: 3