Haibo Yang
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(Fall 2024) |
Personnel
Instructor: Haibo Yang, Assistant
Professor, Dept. of Computing and Information Sciences Ph.D.
Contact: Rm 74-1073, hbycis@rit.edu
Time & Location: MoWeFr 1:00PM - 1:50PM (08/26/2024 - 12/19/2024),
Office Hours: Wed 1:50PM – 2:50PM
TA: TBD
TA Hours: TBD
Course Description
An introduction to both foundational and modern machine learning theories and algorithms, and their application in classification and regression. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), Bayesian decision theory, parametric and nonparametric classification models (e.g., SVMs and Nearest Neighbor models) and neural network models (e.g. Convolutional, Recurrent, and Deep Neural Networks). Programming assignments are required.
|
(Spring 2024) |
Personnel
Instructor: Haibo Yang, Assistant
Professor, Dept. of Computing and Information Sciences Ph.D.
Contact: Rm 74-1073, hbycis@rit.edu
Time & Location: TuTh 2:00PM -- 3:15PM, Golisano Hall (GOL)-2455
Office Hours: Th 3:15PM – 4:15PM
Course Description
This course will introduce algorithm design and convergence analysis in non-convex optimization, with a strong emphasis on their practical applications in addressing contemporary challenges in machine learning and data science. The goal of this course is to prepare graduate students with a solid theoretical foundation at the intersection of optimization and machine learning so that they will be able to use optimization to solve advanced machine learning problems and/or conduct advanced research in the related fields. This course will focus on topics in nonconvex optimization that are of special interest in the machine learning community. Topics covered include large-scale distributed learning (for foundation models and large language models), federated learning, multi-task learning, and differentially private machine learning.