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Computer Science (Sci) : Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.
Terms: Fall 2024, Winter 2025
Instructors: Pr茅mont-Schwarz, Isabeau; Rabbany, Reihaneh (Fall) Li, Yue (Winter)
Prerequisite(s): MATH 323 or ECSE 205, COMP 202, MATH 133, MATH 222 (or their equivalents).
Restriction(s): Not open to students who have taken or are taking COMP 451, ECSE 551, MATH 462, or PSYC 560.
Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526, but not required.
