MS in Artificial Intelligence

Read more about how BU is tackling the coronavirus crisis here

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

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