Description
COMP 435. INTRODUCTION TO MACHINE LEARNING. Machine learning is an essential part of technologies such as image recognition, social network analysis, and autonomous vehicles. This course introduces concepts and algorithms that enable computers to learn from experience. Emphasis is on the practical application of the algorithms, with some discussion of the underlying mathematics. Techniques covered include supervised learning (linear and logistic regression, decision trees, support vector machines, and neural networks), unsupervised learning (clustering and dimensionality reduction), and time-series data (e.g., hidden Markov models or reinforcement learning). Prerequisites: Computer Science 222; and either Mathematics 214 or both Mathematics 222 and one of Statistics 331, Psychology 201 or Statistics 131. Alternate years, spring semester only, three hours.