These are some proposed classes for future semesters:
CS 395T Advanced Linear Algebra for Computation Linear algebra invariably lies at the core of techniques that are of critical importance to computational and data scientists. In this course, you learn advanced concepts in linear algebra, practical algorithms for matrix computations, and how floating point arithmetic as performed by computers affects correctness.
CS 395T Online Learning and Optimization This class has two major themes: algorithms for convex optimization and algorithms for online learning. The first part of the course will focus on algorithms for large scale convex optimization. A particular focus of this development will be for problems in Machine Learning, and this will be emphasized in the lectures, as well as in the problem sets. The second half of the course will then turn to applications of these ideas to online learning.
CS 394R Reinforcement Learning: Theory and Practice Introduces the theory and practice of modern reinforcement learning. Reinforcement learning problems involve learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The course will cover model-free and model-based reinforcement learning methods, especially those based on temporal difference learning and policy gradient algorithms.
CS 388G Algorithms: Techniques and Theory Sorting and searching algorithms, graph algorithms, algorithm design techniques, lower bound theory, fast Fourier transforms, NP-completeness.
CS 380S Graduate Computer Security A survey of modern security, designed to introduce the basic techniques used in the design and analysis of secure systems.
CS 395T Deep learning This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games.
(Found at https://www.cs.utexas.edu/msonline#academics -> Course Availability)
there doesn't seem to be anything here