UT Austin Artificial Intelligence Degree – Trailblazer?

Last week’s announcements about the University of Texas at Austin (UT Austin) master’s degree in Artificial Intelligence offered online through edX at $10,000 read like this was a first of its kind degree that would train thousands of people at an affordable price. Given my interest in the field, I thought I would dig down a little deeper and find out more.

The UT degree will be offered by the Department of Computer Science and Machine Learning Laboratory according to UT News. UT News further added that AI master’s degrees from UT’s peer institutions cost five to ten times as much as UT’s new degree and serve only dozens of students. If this is as successful as Georgia Tech’s $10,000 computer science degree partnership with Udacity and AT&T, UT Austin will have a winning program for it and for its future students. One of the bad pieces of news from the announcement is that the program won’t be available for student enrollments until Spring of 2024.

I visited the website where more information is provided about the new degree. There are 10 courses listed. The names of the courses and the course descriptions below were copied from the website on January 28, 2023.

Ethics in Artificial Intelligence

The goal of this course is to prepare AI professionals for the important ethical responsibilities that come with developing systems that may have consequential, even life-and-death, consequences. Students first learn about both the history of ethics and the history of AI, to understand the basis for contemporary, global ethical perspectives (including non-Western and feminist perspectives) and the factors that have influenced the design, development, and deployment of AI-based systems. Students then explore the societal dimensions of the ethics and values of AI. Finally, students explore the technical dimensions of the ethics and values of AI, including design considerations such as fairness, accountability, transparency, power, and agency.

Planning, Search, and Reasoning Under Uncertainty

This course introduces students to three key foundational problems in AI: planning, search, and reasoning under uncertainty. Beginning with planning domains and algorithms, we will move through classical and modern approaches to planning, including real-world applications of autonomous systems and use of search to find efficient solutions to planning domains with infinite state lengths. We will then shift focus to reasoning about sensing and actuation, and how to model uncertainty.

Automated Logical Reasoning

This is a course on computational logic and its applications in computer science, particularly in the context of software verification. Computational logic is a fundamental part of many areas of computer science, including artificial intelligence and programming languages. This class introduces the fundamentals of computational logic and investigates its many applications in computer science. Specifically, the course covers a variety of widely used logical theories and looks at algorithms for determining satisfiability in these logics as well as their applications.

Case Studies in Machine Learning

The Case Studies in Machine Learning course presents a broad introduction to the principles and paradigms underlying machine learning, including presentations of its main approaches, overviews of its most important research themes and new challenges faced by traditional machine learning methods. This course highlights major concepts, techniques, algorithms, and applications in machine learning, from topics such as supervised and unsupervised learning to major recent applications in housing market analysis and transportation. Through this course, students will gain experience by using machine learning methods and developing solutions for a real-world data analysis problems from practical case studies.

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.

Part 1 covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. The class covers both the theory of deep learning, as well as hands-on implementation sessions in pytorch. In the homework assignments, we will develop a vision system for a racing simulator, SuperTuxKart, from scratch.

Part 2 covers a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. In the homework assignments, we develop a vision system and racing agent for a racing simulator, SuperTuxKart, from scratch.

Natural Language Processing

This course focuses on modern natural language processing using statistical methods and deep learning. Problems addressed include syntactic and semantic analysis of text as well as applications such as sentiment analysis, question answering, and machine translation. Machine learning concepts covered include binary and multiclass classification, sequence tagging, feedforward, recurrent, and self-attentive neural networks, and pre-training / transfer learning.

Online Learning & 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.


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.

Principles of Machine Learning

This course focuses on core algorithmic and statistical concepts in machine learning.

Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.

Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.

Reinforcement Learning: Theory & Practice

This course 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. 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. It covers the essentials of reinforcement learning (RL) theory and how to apply it to real-world sequential decision problems. Reinforcement learning is an essential part of fields ranging from modern robotics to game-playing (e.g. Poker, Go, and Starcraft). The material covered in this class will provide an understanding of the core fundamentals of reinforcement learning, preparing students to apply it to problems of their choosing, as well as allowing them to understand modern RL research. Professors Peter Stone and Scott Niekum are active reinforcement learning researchers and bring their expertise and excitement for RL to the class.

I have been a fan of artificial intelligence (AI) for years. I like to think that my collection of books about the topic (somewhere between 25 and 30) is large for someone who is not an AI programmer. I have purchased and read six books on the topic of AI Ethics, so I was glad to see that as the first course listed.

The description for the remaining courses looks to be extremely relevant based on my limited hands-on experience. When I was president of APUS, we implemented software from Civitas Learning that used AI algorithms to identify patterns that indicated when a student was at risk of dropping out of class or school. We also implemented a software package from Analytikus that used AI algorithms to compare learning and teaching activities to aid in course redesign and improvement. Both of those implementations took longer than I imagined, primarily because of the work required to prepare the data lake that the AI software would utilize to “learn” from. A quickly read overview of how colleges and universities are using AI can be found here.

When I read the courses descriptions for the last five classes, I returned to the program website to see what the prerequisites were for the course. As far as I can tell, you must have a bachelor’s degree with no additional specifications. At the same time, when I read linear programming and convex optimization, I recalled my linear programming classes in the 1970’s. Those classes required a fairly deep understanding of math, and calculus was a prerequisite. I haven’t used calculus or linear programming in years. I am curious if UT Austin will add calculus as a prerequisite. I wonder how many students will hit a wall when they get to these back-end courses that appear to require a high level of mathematical knowledge. At the same time, perhaps the program will be selective in who is admitted, and admissions reps will review the applicants’ transcripts for evidence of mathematical prowess.

I searched Google for online master’s degrees in artificial intelligence. I found the following programs:

Maryville University

Southern Methodist University

Tulane University

Johns Hopkins University

Penn State University

Drexel University

University of Advancing Technology

According to Study Portals, there are 126 online master’s degrees in artificial intelligence. Between the ones that surfaced early in my Google search and the ones listed on Study Portals’ website, it appears there are plenty of opportunities to earn a degree or a certificate in artificial intelligence. I would hope that someone interested in pursuing a degree like UT’s would take the time to research the curriculum and cost of other options.

It will be interesting to see if the UT brand and edX’s marketing abilities can launch this degree as successfully as Georgia Tech, Udacity, and AT&T did. There’s no doubt that Artificial Intelligence is a high demand degree program. How UT Austin differentiates itself will make all the difference in how many students enroll.

Subjects of Interest


Higher Education

Independent Schools


Student Persistence