The curriculum incorporates foundational coursework that provides a broad understanding of the field and elective coursework on subject matter that is in high demand within industry (e.g. advanced systems design, machine learning and artificial intelligence ) that will allow students to tailor their studies to their own interests. Students receive weekly courses through the edX platform, and are advised and assessed by UT Austin faculty and staff on rigorous assignments, programming projects, and comprehensive exams that blend computer science theory and applied, project-based learning using real-world tools and environments. Students also participate in peer grading of assignments to ensure a firm grasp on the material.
At Your Own Pace
Built to provide maximum flexibility, whether you’re a full-time student or a working professional, the online master’s in computer science was designed to enable students to further their education on their own terms. Many people will complete the degree within two to three years, but you can take longer if needed.
Online program students will enjoy the same rigorous training and the same credential as our existing top-ten-ranked graduate program. The resulting degrees will be indistinguishable.
Our courses will teach you not only the methods and techniques you will use in computer science, but also the mathematical foundations of why those methods and techniques are appropriate. Our faculty want you to leave not only knowing how to do something, but also why it works, and why that method is better than others.
This is a 30-hour program with 9 hours of required courses and 21 hours of electives. One class from each course category—Theory, Systems and Applications—are required. Elective courses are opportunities to specialize in areas such as advanced systems design, machine learning and artificial intelligence.
One class from each of the following:
- Applications: Machine Learning, Reinforcement Learning: Theory and Practice, Linear Algebra, Deep Learning, NLP
- Systems: Advanced Operating Systems, Parallel Systems, Android Programming
- Theory: Advanced Linear Algebra for Computing, Algorithms: Techniques and Theory, Optimization, Online Learning and Optimization, Automated Logical Reasoning
- Quantum Information Science
- Structured Implementation of Modern Programming Languages
- Case Studies in Machine Learning
- Note: Any course from the Required class list can be counted towards Elective hours after required course requirements are met.
Algorithms: Techniques and Theory
Vijaya Ramachandran & Greg Plaxton
Online Learning and Optimization
Constantine Caramanis & Sanjay Shakkottai
Reinforcement Learning: Theory and Practice
Peter Stone & Scott Niekum
Courses are offered by semester and follow The University of Texas at Austin academic calendar. Students may begin courses in the semester they applied for admissions (either the fall or spring semester). Students are required to be enrolled in the long semester, fall and spring semesters, whereas the summer semester is optional.
Students may enroll in the MCSO program on a part-time or full-time basis. For working professionals, we recommend taking one to two courses per semester.