INFSCI 2595 MACHINE LEARNING
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
Please click the headings below to view the hidden sections.
|27049 (1050)||W||12:00 pm - 2:50 pm||IS 403||J. Yurko||LEC|