MS in AI

A total of eight graduate courses (totaling 32 credits) must be completed. Students must take the four required core courses:

• GRS CS 640 Artificial Intelligence
• CAS CS 542 Machine Learning
• CAS CS 585 Image and Video Computing
• CAS CS 505 Introduction to Natural Language Processing

The remaining four 4-credit courses may be elected from the following lists of courses. At least one course from list A must be chosen:

List A:

• CAS CS 504: Data Mechanics
• CAS CS 506: Computational Tools for Data Science
• CAS CS 562: Advanced Database Applications
• CAS CS 565: Algorithmic Data Mining
• CAS CS 660: Graduate Introduction to Database Systems

List B:

List B includes all CAS 500+ and GRS courses in computer science. As of fall 2018, these courses are:

• CAS CS 507: Introduction to Optimization in Computing and Machine Learning
• CAS CS 511: Formal Methods 1
• CAS CS 512: Formal Methods 2
• CAS CS 520: Programming Languages
• CAS CS 525: Compiler Design Theory
• CAS CS 530: Analysis of Algorithms
• CAS CS 531: Advanced Optimization Algorithms
• CAS CS 532: Computational Geometry
• CAS CS 533: Spectral Methods for Machine Learning and Network Analysis
• CAS CS 535: Complexity Theory
• CAS CS 537: Randomness in Computing
• CAS CS 538: Fundamentals of Cryptography
• CAS CS 548: Advanced Cryptography
• CAS CS 552: Introduction to Operating Systems
• CAS CS 558: Computer Networks Security
• CAS CS 568: Applied Cryptography
• CAS CS 581: Computational Fabrication
• CAS CS 591: Topics in Computer Science (any section)
• CAS CS 591 K1: Topics in Computer Science: Deep Learning
• GRS CS 611: Object-Oriented Design in Java
• GRS CS 630: Algorithms for MS Students
• GRS CS 651: Distributed Systems
• GRS CS 655: Graduate Computer Networks
• GRS CS 680: Graduate Introduction to Computer Graphics
• GRS CS 940 and 941: Directed Study: Artificial Intelligence

List C:

List C includes AI-related courses taught in the graduate programs of the Mathematics & Statistics and the Electrical & Computer Engineering Departments:

• GRS MA 751 Statistical Machine Learning
• GRS MA 681 Accelerated Introduction to Statistical Methods for Quantitative Research
• ENG EC500 Optimization for Machine Learning
• ENG EC 503 Introduction to Learning from Data
• ENG EC 520 Digital Image Processing and Communication
• ENG EC 719 Statistical Pattern Recognition
• ENG EC 720 Digital Video Processing

Thesis

While not required, MS in AI candidates may complete a master’s project or thesis. Projects are carried out with the approval of, and under the supervision of, a faculty member. A master’s project can be expanded into a master’s thesis, which requires two faculty readers.

3-Semester Sample Program

1^st Semester (Fall):

• CS 640 Artificial Intelligence
• CS 660: Graduate Introduction to Database Systems
• CS 591 S1: Fairness in AI

2^nd Semester (Spring):

• CS 585: Image and Video Computing
• CS 505: Natural Language Processing
• CS 542: Machine Learning

3^rd Semester (Fall):

• CAS CS 523: Deep Learning
• CS 940: Directed Study in AI

2-Semester Sample Program

1^st Semester (Fall):

• CS 640 Artificial Intelligence
• CS 542: Machine Learning
• CS 680: Computer Graphics
• CS 660: Graduate Introduction to Database Systems

2^nd Semester (Spring):

• CS 585: Image and Video Computing
• CS 505: Natural Language Processing
• CAS CS 523: Deep Learning
• CS 940: Directed Study in AI

Please view our Admissions tab on how to apply.