INFSCI 2595 MACHINE LEARNING
Description
Minimum Credits: 3Maximum Credits: 3
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
Current Sections
Spring 2021
Class No. | Days | Times | Room | Instructor(s) | TA(s) | Type | Session |
---|---|---|---|---|---|---|---|
27529 (1010) | W | 12:10 pm - 3:00 pm | WPU G18 | J. Yurko | LEC | AT |
Future Sections
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Fall 2021
Class No. | Days | Times | Room | Instructor(s) | TA(s) | Type | Session |
---|---|---|---|---|---|---|---|
27049 (1050) | W | 12:00 pm - 2:50 pm | IS 403 | J. Yurko | LEC | AT |
Past Sections
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Fall 2020
Class No. | Days | Times | Room | Instructor(s) | TA(s) | Type | Session |
---|---|---|---|---|---|---|---|
26748 (1010) | Tu | 6:30 pm - 9:20 pm | UCLUB 211 | J. Yurko | LEC | AT |
Spring 2020
Class No. | Days | Times | Room | Instructor(s) | TA(s) | Type | Session |
---|---|---|---|---|---|---|---|
28698 (1010) | W | 12:00 pm - 2:50 pm | VICTO 115 | J. Yurko | LEC | AT |