Artificial Intelligence Course

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Last Update May 16, 2022
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About This Course

Artificial Intelligence, AI, is an area of study that explores how to create computer programs that learn to make decisions, reason about data, and communicate with humans. In the MS in AI degree program, students learn to apply creative thinking, algorithmic design, and coding skills to build modern AI systems. Students will gain deep technical training and expertise in our focus areas of machine learning, computer vision, and natural language processing.

The program prepares students to work as Artificial Intelligence Engineers in information technology companies or pursue a Ph.D. degree in computer science.

The 8-course MS in AI program is geared toward students with a computer science undergraduate degree, but we also welcome those with equivalent training and experience, as well as students with gaps in their computing background but strong academic records overall.

Program Description
Artificial Intelligence, AI, is an area of study that explores how to create computer programs that learn to make decisions, reason about data, and communicate with humans. In the MS in AI degree program, students learn to apply creative thinking, algorithmic design, and coding skills to build modern AI systems. Students will gain deep technical training and expertise in our focus areas of machine learning, computer vision, and natural language processing.

The program prepares students to work as Artificial Intelligence Engineers in information technology companies or pursue a Ph.D. degree in computer science.

The 8-course MS in AI program is geared toward students with a computer science undergraduate degree, but we also welcome those with equivalent training and experience, as well as students with gaps in their computing background but strong academic records overall.

Requirements
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 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 679 Applied Statistical Machine Learning
GRS MA 751 Statistical Machine Learning
GRS MA 681 Accelerated Introduction to Statistical Methods for Quantitative Research
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
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Sample Program
3-Semester Sample Program

1st Semester (Fall):

CS 640 Artificial Intelligence
CS 660: Graduate Introduction to Database Systems
CAS CS 507: Introduction to Optimization in Computing and Machine Learning
2nd Semester (Spring):

CS 585: Image and Video Computing
CS 505: Natural Language Processing
CS 542: Machine Learning
3rd Semester (Fall):

CS 591 K1: Deep Learning
CS 940: Directed Study in AI
2-Semester Sample Program

1st Semester (Fall):

CS 640 Artificial Intelligence
CS 542: Machine Learning
CS 680: Computer Graphics
CS 565: Algorithmic Data Mining
2nd Semester (Spring):

CS 585: Image and Video Computing
CS 505: Natural Language Processing
CS 591 K1: Deep Learning
CS 940: Directed Study in AI

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