DSC 255R. Machine Learning Fundamentals (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/DSC.html#dsc255r
Description
Supervised and unsupervised learning algorithms, and the theory behind those algorithms. Using case studies, covered topics include classification, regression, and conditional probability estimation; generative and discriminative models; linear models and extensions to non-linearity using kernel methods; ensemble methods: boosting, bagging, random forests; representation learning: clustering, dimensionality reduction, auto-encoders, deep neural networks. This is a distance education course. Prerequisites: DSC 215R. Restricted to major code DS77. All other students with graduate standing may be considered as space permits.
Prerequisite courses
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Successor courses
DSC 255R is a prerequisite of the following 6 courses: