COMSC 415 - Machine Learning

MATH 225   or MATH 255   and MATH 301   or higher or permission of instructor
Machine learning is the study of how to build computer systems that learn from data in order to make predictions, recognize patterns, and organize information. This course will explore both the underlying mathematical theory and practical application of methods for machine learning. Topics include supervised and unsupervised learning, dimensionality reduction, support vector machines, decisions trees, clustering, neural networks. In addition, students will use advanced deep learning techniques to build models using large scale data. Potential implementations include image recognition, signal processing, time series forecasting, recommender systems, reinforcement learning, computer vision, and sentiment analysis. Multiple case studies will be drawn from real-word applications including business, science, engineering, bioinformatics, healthcare, political science, epidemiology, and public health.

3 credits
Alternate Spring