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Dec 15, 2025
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MATH 355 - Machine Learning
Cross-Listed with: COMSC 415
Pre- or Co-requisite: MATH 255 or MATH 225 and MATH 301 or higher, or permission of instructor
Requirement Fulfillment: Major, Minor, Core Concentration
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 the practical application of methods for machine learning. Topics include supervised and unsupervised learning, dimensionality reduction, support vector machines, decisions trees, clustering, and 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-world applications including business, science, engineering, bioinformatics, healthcare, political science, epidemiology, and public health.
3 credits
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