Program Overview

Master’s of Artificial Intelligence

The Master’s of Artificial Intelligence at UT Austin is a 30-hour program designed to prepare students to stand out in the fast-growing field of AI. The program covers a range of highly sought-after skills, including reasoning under uncertainty, ethics in AI, case studies in machine learning, and more.

Curriculum

The online AI master’s coursework is designed to be accessed on a flexible schedule, featuring on-demand lectures and weekly release schedules. The program consists of 3 hours of required courses and 27 hours of electives, with each course counting for 3 credit hours. Students must take a total of 10 courses to graduate.

Required and Foundational Courses

• Deep Learning: This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics, and unsupervised feature learning.
  • Part 1: Covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks.
  • 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.
• Ethics in AI: This course prepares AI professionals for the important ethical responsibilities that come with developing systems that may have consequential, even life-and-death, consequences.
  • Students learn about the history of ethics and the history of AI, to understand the basis for contemporary, global ethical perspectives.
  • The course explores the societal dimensions of the ethics and values of AI, as well as the technical dimensions, including design considerations such as fairness, accountability, transparency, power, and agency.
• Machine Learning: This course focuses on core algorithmic and statistical concepts in machine learning, including 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.
• Planning, Search, and Reasoning Under Uncertainty: This course investigates how to define planning domains, including representations for world states and actions, covering both symbolic and path planning.
• Reinforcement Learning: This course introduces the theory and practice of modern reinforcement learning, including model-free and model-based reinforcement learning methods.

Elective Courses

• Advances in Deep Generative Models: This course explores modern techniques for generative modeling, focusing on both the theoretical underpinnings and the latest advancements in the field.
• Advances in Deep Learning: This course provides an in-depth exploration of the technologies behind some of the most advanced deep learning models.
• AI in Healthcare: This course explores the major components of health IT systems and how AI innovations are transforming the healthcare system.
• Automated Logical Reasoning: This course introduces the fundamentals of computational logic and its applications in computer science, particularly in the context of software verification.
• Case Studies in Machine Learning: This course presents a broad introduction to the principles and paradigms underlying machine learning, including presentations of its main approaches and overviews of its most important research themes.
• Natural Language Processing: This course focuses on modern natural language processing using statistical methods and deep learning.
• Online Learning and Optimization: This course covers algorithms for convex optimization and algorithms for online learning.
• Optimization: This class covers linear programming and convex optimization, including formulation and geometry of LPs, duality and min-max, primal and dual algorithms for solving LPs.

Important Dates

• Fall Application:
  • Application Opens: December 15
  • Priority Deadline: March 15
  • Final Deadline: April 15
• Spring Application:
  • Application Opens: June 1
  • Priority Deadline: August 1
  • Final Deadline: September 1