M.S. in Artificial Intelligence

The M.S. program in Artificial Intelligence acclimates students to the ongoing AI revolution that has already produced computer programs with problem-solving and content-generating abilities that complement and enhance human abilities. The program offers theoretical and practical knowledge in various areas of AI, including Natural Language Understanding and Generation, Image Understanding, Reasoning, and Planning. It empowers students to apply AI techniques in a wide range of application domains. 

Prerequisites

Applicants should have a bachelor's degree in the general area of Computing, from an accredited University. Applicants with a bachelor's degree in STEM or related professional experience can start with the graduate certificate and then apply to the M.S. program. Further information can be found in the program's webpage. 

Degree Requirements 

The program requires the completion of 30 credits. These are satisfied by taking 10 courses, consisting of 4 core courses and 6 elective courses, as indicated in the following table.

If a student chooses the MS thesis option, the thesis must be related to Artificial Intelligence and will require approval from the Program Director and the thesis advisor.

Students may choose elective courses outside of the list shown below with approval from their Academic Advisor (or Program Director).

Course List

Code	Title	Credits
Core Courses
Required
MATH 662	Probability Distributions
DS 644	Introduction to Big Data
DS 675	Machine Learning
or CS 670	Artificial Intelligence
Select one of the following:
DS 669	Reinforcement Learning
DS 677	Deep Learning
DS 680	Natural Language Processing
DS 683	Graph Neural Networks
DS 789	Trustworthy Artificial Intelligence
Any of the remaining core courses listed can count towards the elective requirements.
Elective Courses
1. Data Science (at least two courses)
DS 633	Advances in Representation Learning
DS 637	Python and Mathematics for Machine Learning *
DS 669	Reinforcement Learning
DS 677	Deep Learning
DS 680	Natural Language Processing
DS 683	Graph Neural Networks
DS 688	Advanced Federated Machine Learning
DS 698	Special Emerging Topics
DS 732	Theoretical Foundation of Machine Learning
or CS 732	Advanced Machine Learning
DS 786	Selected Topics in Data Science
DS 789	Trustworthy Artificial Intelligence
2. Computer Science (at most one course)
CS 634	Data Mining
CS 659	Image Processing and Analysis
CS 681	Computer Vision
CS 782	Pattern Recognition and Applications
3. Statistics (at most one course)
MATH 663	Introduction to Biostatistics
MATH 665	Statistical Inference
MATH 678	Statistical Methods in Data Science
4. Multi-disciplinary Applications (at most two courses)
Robotics:
ME 625	Introduction to Robotics
BME 673	Biorobotics
BME 676	Computational Biomechanics
BME 760	Modeling in Func Brain Imaging
Engineering:
ECE 744	Optimization for Data Engineering
ECE 776	Information Theory
BME 661	Neural Engineering
MTEN 633	Machine Learning for Chemical and Materials Engineers
CE 506	Remote Sensing of Environment
CE 739	Structural Optimization
Science:
BIOL 601	Computational Biology I
BIOL 635	Intro to Comp Neuroscience
BIOL 636	Advanced Comp Neuroscience
BIOL 638	Computational Ecology
BIOL 672	Computational Systems Biology
CHEM 716	Integrated Drug Dev & Discover
CHEM 737	Applications of Computational Chemistry and Molecular Modeling
Business and Management:
FIN 616	Data Driven Financial Modeling
ACCT 640	Big Data Analytics for Accounting
MGMT 630	Decision Analysis with Quantitative Modeling
MGMT 735	Deep Learning in Business
Project and Thesis Courses
DS 700B	Master's Project
DS 701B	Master's Thesis

*

DS 637 is recommended as an introductory course, offering a review of mathematics for machine learning to students with a limited background in mathematics or programming.

Master's Project and Thesis Policies

The contents of this section apply only to students who elect to do a DS 700B Master's Project or a DS 701B Master's Thesis in topics related to Artificial Intelligence.

Students must first find a research advisor who is a tenure-track faculty member of the DS department, including faculty with a joint appointment. Tenure-track faculty members are department members, including those who hold joint appointments, with the rank of Assistant Professor, Associate Professor, Professor, or Distinguished Professor.

To find a research advisor, students are encouraged to attend special presentations offered by the department or to contact professors directly. Professors may not always be available to conduct an MS project/thesis. Students are, therefore, encouraged to start looking for an advisor as early as possible, especially if they are considering pursuing a Master's Project followed by a Master’s Thesis in two semesters.

The students must work closely with their research advisor, who will determine the topic of the Project/Thesis and guide them through specific core and elective courses that will prepare them for the research.

Registration:

• Master’s Project: With permission of their research advisor, students must register in the DS 700B Master's Project course. To register for the Master's Project, students must have completed at least 9 credits and must be in good standing.
• Master’s Thesis: With permission of their research advisor, students must register in the DS 701B Master's Thesis course. 
• They must receive a satisfactory (S) grade in DS 700B before DS 701B registration in the immediately following semester, with the same advisor. The MS thesis topic should be continuation of the work done in DS 700B.

* Students must meet with their academic advisor for before course registration.

Thesis Requirements:

• An MS Thesis Committee must be formed, according to the requirements set forth by the Office of Graduate Studies.
• A written thesis must be submitted. The thesis must adhere to the style requirements set forth by the Office of Graduate Studies.
• An oral defense is required. The defense must take place before the last day of the Final Examinations.