Introduction to machine learning, includes algorithms of supervised and unsupervised machine learning techniques, designing a machine learning system, bias-variance tradeoffs, evaluation metrics; Parametric and non-parametric algorithms for regression and classification, k-nearest-neighbor estimation, decision trees, discriminant analysis, neural networks, deep learning, kernels, support vector machines, ensemble methods, regularization techniques; Dimensionality reduction, principle component analysis, LDA, t-SNE; Clustering methods such as k-means, hierarchical clustering, spectral clustering, DBSCAN; Mathematical foundations including linear algebra, probability theory, statistical tests, statistical learning theory; Best practices and application to real-world problems.

Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis
Minimum Credits: 3
Maximum Credits: 3

Current Sections

Spring 2023

Class No.DaysTimesRoomInstructor(s)TA(s)Type
29318 (1000)Tu12:00 pm - 2:50 pmIS 403J. Yurko