The recognition is for a 2005 paper titled “Agnostically Learning Halfspaces,” which Klivans co-authored with Adam Tauman ...

Researchers at The University of Texas at Austin recently received support from the National Science Foundation (NSF) to ...

Experience programming, teamwork, and real-world applications in this week-long residential program for high school students. Choose from Arduino microcontrollers in the Standard Edition or Python in ...

Get an inside look at the creative and technical sides of game development. See how design studios operate, meet UT faculty and industry professionals, and explore future paths in game development.

Explore coding, project management, and teamwork through C++ and collaborative projects. Learn from UT faculty and current students while discovering the impact of women in tech. Live on campus, grow ...

Study computer science in a program that’s both rigorous and flexible. At UT Austin, you’ll build a strong foundation in computing and shape your degree around what interests you most. Our curriculum ...

Artificial Intelligence and Life in 2030. Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram ...

Through hands-on projects and mentorship from UT faculty, you’ll dive into coding with Python to train your own models, test their performance, and explore the ethical and social impacts of machine ...

Learn to build mobile apps using Swift and Xcode while developing a strong foundation in user interface design, logic, and collaboration. Work alongside peers, guided by UT faculty and industry ...

We know that managing costs and minimizing student debt are important factors in your UT experience. Use the information below to find details on estimated cost of attendance, tuition rates, financial ...

Combine computer science with the field that inspires you most. The X+CS Integrated Degree Program connects disciplines, expands your perspective and prepares you to take on the complex challenges ...

Deep Recurrent Q-Learning for Partially Observable MDPs Deep Recurrent Q-Learning for Partially Observable MDPs. Matthew Hausknecht and Peter Stone. In AAAI Fall Symposium on Sequential Decision ...