# 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

## Past Sections

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### Fall 2021

Class No. | Days | Times | Room | Instructor(s) | TA(s) | Type |
---|---|---|---|---|---|---|

27049 (1050) | W | 12:00 pm - 2:50 pm | IS 403 | J. Yurko | LEC |