I don't have an answer to your question.

Since they haven't started the course yet, I doubt anyone has direct experience,

but the curriculum for the fall 2019 fall offering is (from their home page [1] )

1. CS 391L Machine Learning
Adam Klivans and Qiang Liu

Computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement.

2. CS 380L Advanced Operating Systems -Vijay Chidambaram

Study of the formal structure, design principles, organization, implementation, and performance analysis of multiprogramming and/or multiprocessor computer systems.

3. CS 380P Parallel Systems - Calvin Lin and Chris Rossbach

The objective of this course is to provide students with strong background on concurrency fundamentals along with experience with a diversity of both classical and modern approaches to managing and exploiting concurrency, including shared memory synchronization, parallel architectures such as GPUs, as well as distributed parallel frameworks such as MPI and map-reduce. Material will be presented through readings and discussion of background material as well as occasional recent research papers when appropriate. The course requires a number of programming and project assignments to provide direct experience with design, programming, and measurement methodologies for concurrent systems.

4. CS 395T Optimization -Sujay Sanghavi

This class covers Linear Programming and Convex Optimization. These are fundamental conceptual and algorithmic building blocks for applications across science and engineering. Indeed any time a problem can be cast as one of maximizing / minimizing and objective subject to constraints, the next step is to use a method from linear or convex optimization. Covered topics include formulation and geometry of LPs, duality and min-max, primal and dual algorithms for solving LPs, Second-order cone programming (SOCP) and semidefinite programming (SDP), unconstrained convex optimization and its algorithms: gradient descent and the newton method, constrained convex optimization, duality, variants of gradient descent (stochastic, subgradient etc.) and their rates of convergence, momentum methods.

5. CS 395T Deep learning -Philipp Krähenbühl

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.

(Spring 2020)

6. CS 395T Advanced Linear Algebra for Computation
Maggie Myers & Robert van De Geijn

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.

7. CS 388G Algorithms: Techniques and Theory
Vijaya Ramachandran and Greg Plaxton

Sorting and searching algorithms, graph algorithms, algorithm design techniques, lower bound theory, fast Fourier transforms, NP-completeness

8. CS 395T Online Learning and Optimization
Constantine Caramanis and Sanjay Shakkottai
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.

9.
CS 394R Reinforcement Learning: Theory and Practice
Peter Stone and Scott Niekum
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.

assuming these are the equivalent of the offline courses, in terms of instruction, exams/assignments, and have decent instructor access, if not at quite the level of an on campus education, looks solid enough to me.

[1] https://www.cs.utexas.edu/msonline