Courses

(Prereq. DSC 207R and DSC 215R)

Fundamentals of supervised and unsupervised learning algorithms, and the theory behind those algorithms. Application of techniques utilizing Python and Jupyter notebooks through real-world case studies. Classification, regression, and conditional probability estimation; Generative and discriminative models; Linear models and extensions to nonlinearity using kernel methods; Ensemble methods: boosting, bagging, random forests; Representation learning: clustering, dimensionality reduction, autoencoders, deep neural networks.