Master of Science, Applied Computer Science Curriculum
The MSCS is 30 credits. Students are required to take 3 foundational classes, plus a two-semester thesis. In addition, they may take 3 AI electives (see list below) plus one graduate-level computer science course (ACS Elective) and 1 graduate-level course from any discipline (Open Elective) with approval from their advisor.
All courses are proposed and subject to change. For the most up-to-date curriculum, contact a member of the Graduate Admissions team.
4+1 Option
Spring |
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|
Course Title/Subject |
Credits |
Modality |
|
Modern Computing |
4 |
Campus |
Summer |
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|
Course Title/Subject |
Credits |
Modality |
|
Classical AI |
4 |
Campus |
|
|
|
|
Fall – Grad Semester 1 |
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|
Course Title/Subject |
Credits |
Modality |
|
Data Mining |
3 |
Campus |
|
OR Principles of Machine Learning |
3 |
Campus |
|
AI Elective |
3 |
Campus |
|
AI Elective |
3 |
Campus |
|
Thesis I |
3 |
Campus |
Spring – Grad Semester 2 |
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|
Course Title/Subject |
Credits |
Modality |
|
AI Elective |
3 |
Campus |
|
ACS Elective |
3 |
Campus |
|
Open Elective |
3 |
Campus |
|
Thesis II |
3 |
Campus |
AI Electives |
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|
Course Title/Subject |
Credits |
Modality |
|
Advanced Parallel Processing |
3 |
Campus |
|
Computer Vision |
3 |
Campus |
|
Deep Learning |
3 |
Campus |
|
AI for Gaming |
3 |
Campus |
|
Evolutionary Algorithms |
3 |
Campus |
2-Year Option
Fall - Semester 1 |
||
Course Title/Subject |
Credits | Modality |
Modern Computing |
4 | Campus |
Mathematics for M.L. |
3 | Campus |
Open Elective |
3 | Campus |
Spring - Semester 2 |
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Course Title/Subject |
Credits | Modality |
Applied CS Elective |
3 | Campus |
Classical A.I. |
4 | Campus |
A.I. Elective |
3 | Campus |
Summer |
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|
No classes (Optional - Graduate Internship) |
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Fall - Semester 3 |
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Course Title/Subject |
Credits |
Modality |
Data Mining |
3 | Campus |
A.I. Elective |
3 | Campus |
Thesis I |
3 | Campus |
Spring - Semester 4 |
||
Course Title/Subject |
Credits |
Modality |
Thesis II |
3 | Campus |
A.I. Elective |
3 | Campus |
3-Year Option
Fall - Semester 1 |
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|
Course Title/Subject |
Credits |
Modality |
|
Programming Fundamentals |
6 |
Campus |
|
Calculus |
4 |
Campus |
Spring - Semester 2 |
||
|
Course Title/Subject |
Credits |
Modality |
|
Data Structures & Algorithms |
6 |
Campus |
|
Statistics |
4 |
Campus |
Summer |
||
|
No classes (Optional - Graduate Internship) |
Fall - Semester 3 |
||
|
Course Title/Subject |
Credits |
Modality |
|
Modern Computing |
4 |
Campus |
|
Mathematics for Machine Learning |
3 |
Campus |
|
Open Elective |
3 |
Campus |
Spring - Semester 4 |
||
|
Course Title/Subject |
Credits |
Modality |
|
Classical AI |
4 |
Campus |
|
Elective (Applied Computer Science) |
3 |
Campus |
|
Elective (AI) |
3 |
Campus |
Summer |
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|
No classes (Optional - Graduate Internship) |
Fall - Semester 5 |
||
|
Course Title/Subject |
Credits |
Modality |
|
Principles of Machine Learning |
3 |
Campus |
|
Elective (AI) |
3 |
Campus |
|
Thesis I |
3 |
Campus |
Spring - Semester 6 |
||
|
Course Title/Subject |
Credits |
Modality |
|
Elective (AI) |
3 |
Campus |
|
Thesis II |
3 |
Campus |