Computer Science, MS
The Master's Program in Computer Science is offered in coursework and thesis tracks. The coursework option requires both breadth and depth requirements. The breadth requirement ensures students obtain a solid foundation in core computer science areas, while the depth requirement allows students to design a sequence of courses to target a particular area of interest. The thesis track further allows students to conduct research in a chosen area of interest. The Master’s degree can also be pursued in a 4+1 format in conjunction with a major in Computer Science.
The M.S. program requires 30 credit hours of graduate coursework. Coursework requirements vary slightly depending on the chosen track, but consist of 9 credits of core breadth coursework and 21 credits of elective depth coursework. Below we outline these degree tracks with their associated course requirements and provide some example curricula. We note that some of these example curricula do not have full-time enrollment in all semesters. Additional electives can be added in these slots as needed for full-time requirements.
Coursework and Degree Tracks
At the high level, the course requirements for the M.S. can be thought of as having a core requirement and an elective requirement. The core requirement is identical to that of our Ph.D. program, which requires of one core course from each of three breadth areas (Algorithms, Systems and Artificial Intelligence/Machine Learning), for a total of three courses counting for 9 credit hours.
Our three core areas can be fulfilled by the following courses:
- Algorithms: CMPS 6610 Algorithms (3 c.h.)
- Systems: CMPS 6750 Computer Networks (3 c.h.) or CMPS 6770 Operating Systems (3 c.h.) or CMPS 6780 Computer Architecture (3 c.h.)
- AI/ML: CMPS 6620 Artificial Intelligence (3 c.h.) or CMPS 6720 Machine Learning (3 c.h.)
The remaining 21 credits can be fulfilled through one of three degree tracks: coursework, project, and thesis tracks. The coursework and project tracks, but not the thesis track, can be completed in the 4+1 format.
The coursework track can be completed by taking 21 credits of CS graduate electives.
CS graduate electives are CS courses at the 6000-level or higher excluding CMPS 6100 Introduction to Computer Science (3 c.h.), CMPS 6160 Introduction to Data Science (3 c.h.),CMPS 6140 Intro Artificial Intelligence (3 c.h.), CMPS 6240 Intro to Machine Learning (3 c.h.), and CMPS 7010 Research Seminar (3 c.h.). Core courses not counted toward the core requirement can count toward elective requirements. A comprehensive list of current courses can be found on our catalog course list page.
The project track can be completed by taking 15 credits of CS graduate electives and 6 credits of CMPS 7980 Independent Study (3 c.h.) or CMPS 9980 Masters Research (0 to 3 c.h.) over the course of two semesters for a grade. These courses do not culminate in a thesis but do require that project goals and assessments are clearly stated in the syllabi for the courses. Projects are completed under the advisement of a faculty mentor who is the instructor for the project (independent study or research) courses.
Finally, the thesis track can be completed by taking 15 credits of CS graduate electives and 6 credits CMPS 9980 Masters Research (0 to 3 c.h.) over two semesters for a grade. The thesis is supervised by a faculty advisor chosen by the end of the 2nd semester. Students must also form an M.S. Thesis Committee by the end of their 2nd semester. The M.S. Thesis Committee will consist of an advisor, one CS faculty member, and one other SSE faculty member. The final thesis must be presented and approved by the committee prior to the end of their 4th semester.
In all track options we encourage elective choices that are coherent enough to provide a specialized area of study, but flexible enough that students can explore different areas of computer science. Below we give examples of each track with sample curricula.
Coursework Track
Example coursework tracks for 21 credit hours, or 7 CS graduate elective courses are given below.
In some instances, it may be possible to submit a petition to the Graduate Studies Committee to count non-CS courses for elective credit (e.g., in an interdisciplinary subject area). No more than 2 such graduate courses may be counted toward M.S. coursework credit.
Coursework Track: AI/ML Focus
| Course ID | Title | Credits |
|---|---|---|
| AI/ML Focus | ||
| Semester 1 | ||
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 6790 | Data Science | 3 |
| Semester 2 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6720 | Machine Learning | 3 |
| CMPS 6360 | Data Visualization | 3 |
| Semester 3 | ||
| CMPS 6750 | Computer Networks (*) | 3 |
| CMPS 6150 | Multi-agent Systems | 3 |
| CMPS 6730 | Natural Language Processing | 3 |
| Semester 4 | ||
| CMPS 6280 | Information Theory | 3 |
| CMPS 6740 | Reinforcement Learning | 3 |
Coursework Track: Data Science Focus
| Course ID | Title | Credits |
|---|---|---|
| Data Science Focus | ||
| Semester 1 | ||
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 6790 | Data Science | 3 |
| Semester 2 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6350 | Intro to Computer Graphics | 3 |
| CMPS 6300 | Software Studio | 3 |
| Semester 3 | ||
| CMPS 6360 | Data Visualization | 3 |
| CMPS 6280 | Information Theory | 3 |
| CMPS 6750 | Computer Networks (*) | 3 |
| Semester 4 | ||
| CMPS 6720 | Machine Learning | 3 |
| CMPS 6150 | Multi-agent Systems | 3 |
Coursework Track: Algorithms and Theory Focus
| Course ID | Title | Credits |
|---|---|---|
| Algorithms and Theory Focus | ||
| Semester 1 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6280 | Information Theory | 3 |
| Semester 2 | ||
| CMPS 6250 | Math Found Comp Security | 3 |
| CMPS 6310 | Logic in Computer Science | 3 |
| CMPS 6720 | Machine Learning (*) | 3 |
| Semester 3 | ||
| CMPS 6130 | Intro Comp Geom | 3 |
| CMPS 6710 | Computational Complexity | 3 |
| CMPS 6750 | Computer Networks (*) | 3 |
| Semester 4 | ||
| CMPS 6260 | Advanced Algorithms | 3 |
| CMPS 6740 | Reinforcement Learning | 3 |
Project Track
An example project track consisting of 15 credits of CS graduate electives and 6 credits of independent study is given below.
Project-based Track, Data Science Focus
| Course ID | Title | Credits |
|---|---|---|
| Data Science Focus | ||
| Semester 1 | ||
| CMPS 6360 | Data Visualization | 3 |
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 6790 | Data Science | 3 |
| Semester 2 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6350 | Intro to Computer Graphics | 3 |
| CMPS 6280 | Information Theory | 3 |
| Semester 3 | ||
| CMPS 6750 | Computer Networks (*) | 3 |
| CMPS 7980 | Independent Study | 3 |
| Semester 4 | ||
| CMPS 6720 | Machine Learning | 3 |
| CMPS 7980 | Independent Study | 3 |
Thesis Track.
An example thesis track consisting of 15 credits of CS graduate electives and 6 credits of masters research is given below.
Thesis Track, AI/ML Focus
| Course ID | Title | Credits |
|---|---|---|
| AI/ML Focus | ||
| Semester 1 | ||
| CMPS 6280 | Information Theory | 3 |
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 6750 | Computer Networks (*) | 3 |
| Semester 2 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6720 | Machine Learning | 3 |
| CMPS 6150 | Multi-agent Systems | 3 |
| Semester 3 | ||
| CMPS 6730 | Natural Language Processing | 3 |
| CMPS 9980 | Masters Research | 3 |
| Semester 4 | ||
| CMPS 6740 | Reinforcement Learning | 3 |
| CMPS 9980 | Masters Research | 3 |
4+1 Track
Students embarking on a 4+1 masters degree should complete 6-12 credits of graduate credit during their undergraduate degree to count towards their 4+1, leaving 18-24 credits to be completed during their 5th year to finish their masters degree. Six of the graduate credits taken during their undergraduate degree may count towards both their undergraduate and graduate degrees. The remaining 6 credits may count towards only their graduate degree.
Graduate credit taken during undergraduate degree policies
SSE allows at most 6 graduate credit hours to be counted toward both undergraduate and graduate degrees. In our department, advanced undergraduate electives are available as “mezzanine” courses, with undergraduate and graduate sections that can count for undergraduate or graduate credit respectively. For the 4+1 degree program, undergraduate students can count 6 credit hours of graduate credit (6000-level or higher) toward both their CS major as well as an M.S. degree.
Additionally, students may apply up to another 6 credit hours of graduate coursework completed during the undergraduate degree towards their 4+1 degree only. These additional 6 credits may not be used to satisfy any of the requirements of a student's undergraduate degree (including the 120 credit hour minimum) and will apply towards their graduate degree only.
We give two sample curricula below.
4+1 Track: Data Science Focus
Here, we assume that 2 CS graduate electives were taken during their undergraduate degree to count toward the 4+1 degree.
| Course ID | Title | Credits |
|---|---|---|
| Data Science Focus | ||
| Semester 1 | ||
| CMPS 6350 | Intro to Computer Graphics | 3 |
| CMPS 6280 | Information Theory | 3 |
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 6790 | Data Science | 3 |
| Semester 2 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6360 | Data Visualization | 3 |
| CMPS 6750 | Computer Networks (*) | 3 |
| CMPS 6150 | Multi-agent Systems | 3 |
Project-based 4+1 Track: AI/ML Focus
It is possible to incorporate a project into the 4+1 degree program by pursuing project work. Here, we assume that 2 CS graduate electives were taken during their undergraduate degree to count toward the 4+1 degree.
| Course ID | Title | Credits |
|---|---|---|
| AI/ML Focus | ||
| Summer 1 | ||
| CMPS 6620 | Artificial Intelligence (*) | 3 |
| CMPS 7980 | Independent Study | 3 |
| Semester 1 | ||
| CMPS 6610 | Algorithms (*) | 3 |
| CMPS 6280 | Information Theory | 3 |
| Semester 2 | ||
| CMPS 6720 | Machine Learning | 3 |
| CMPS 6740 | Reinforcement Learning | 3 |
| CMPS 6780 | Computer Architecture (*) | 3 |
| Summer 2 | ||
| CMPS 7980 | Independent Study | 3 |
Program String and Field of Study: SEMS_GR, CMPS
For more information, contact the School of Science and Engineering.