COMSC 415 - Machine Learning
Prerequisites: MATH 315 ;
Corequisite: COMSC 111 or ENGR 424
This course is an introduction to the study of how to build computer systems that learn from data in order to make predictions, recognize patterns, and organize information. The course will explore both the theoretical basis and practical application of methods for machine learning, data mining, and statistical data analysis. Topics include supervised and unsupervised learning, generative and discriminative models, neural networks, support vector machines, decisions trees, and clustering. Students will apply these methods to real world data sets from science, engineering, and business.
3 credits
Alternate Spring
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