Artificial Intelligence (AIG) Subplan Requirements (Post Fall 2027)

 

What is a Subplan?

A subplan is an optional enhancement to the Professional MS in Computer Science (MSCPS) degree program allowing students to demonstrate a high level of expertise in a sub-field of Computer Science. 

Current students in the MSCPS program have the option to declare exactly one (1) subplan.

The subplan appears on the student’s transcript as a Specialization. The subplan does not appear on the diploma.

Completing a Subplan

MSCPS students interested in adding a subplan must officially declare their subplan. Subplans are not automatically added, even if a student meets the requirements for the subplan.

If an MSCPS student officially declares a subplan, they are responsible for completing the subplan requirements in order to graduate with the MSCPS degree. If a student has otherwise completed the MSCPS requirements but not the subplan requirements, they will not be eligible to graduate until they complete the subplan requirements or drop the subplan before the deadline.

MSCPS students can declare a subplan after their first semester and no later than the add deadline of their final semester. No more than one change request (add/drop/swap) is permitted, pending extenuating circumstances. To declare a subplan, submit the Degree / Option Change request form (Forms & Policies).

Degree Requirements 

The MSCPS with AIG Subplan aligns with the residential MS in Artificial Intelligence degree program, allowing MSCPS students to choose AI-focused coursework to satisfy their MSCPS degree requirements and add a specialization to their transcript. 

The MSCPS with AIG subplan requires total 30 credit hours of graduate coursework according to the requirements for the AIG subplan:

CS Breadth (BIN) courses (6 credit hours)

  • Successfully complete one 3-credit Computer Science course from each of the following breadth bins: Bin 1 and Bin 3. 
  • Students must earn a B or better (not a B-) in at least one 5000-level course (not 6000 or higher) from each of the two bins.
  • By petition to the Graduate Committee, similar transferred graduate work done elsewhere may be used to satisfy part of this requirement. 
  • Note - Students in this subplan complete the Bin 2 requirement by successfully completing at least one of the AI Core courses with a ‘B’ or higher.

AI Professional Readiness (3 credits) - Required

The THREE one-credit professional readiness sequence equips students with the ethical frameworks, practical software toolkits, and communication skills needed for industry success. By targeting these specific competencies, the courses seamlessly bridge the gap between academic theory and real-world industry execution. 

These three 1-credit courses are offered sequentially during the same semester, running back-to-back for 5 weeks each on the same day and time pattern. Students are required to earn a B or better grade (not B-) in these classes as part of the degree requirement. 

  • CSCI 5011 – AI Professional Skills (1 credit)
  • CSCI 5012 – AI Toolkits (1 credit)
  • CSCI 5013 – AI Ethics (1 credit)

AI Core (9 credits) - Required

Core requirements are designed to accommodate students from a wide variety of academic backgrounds while ensuring that all students gain a common core of knowledge. Students are recommended to complete the core requirement in the first year. All students must earn a B or better in these courses. Students must complete the following core requirement for a total of 9 credit hours. 

  • CSCI 5622 Machine Learning (3 credits)       
  • CSCI 5922 Neural Networks and Deep Learning (3 credits)  
  • CSCI 5942 AI Engineering: Building, Scaling, and Deploying Large-Scale Models (3 credits)          

AI Depth Requirements (6 credits) - Required
Students must complete 6 credit hours from the following CS-Depth course list. All students must earn a B or better in these courses. Select two 3-credit classes from the list below.

  • CSCI 5202 Introduction to Robotics
  • CSCI 5302 Advanced Robotics
  • CSCI 5322 Algorithmic Human-Robot Interaction
  • CSCI 5352 Network Analysis and Modeling
  • CSCI 5434 Probability for Computer Science 
  • CSCI 5502 Data Mining
  • CSCI 5722 Computer Vision
  • CSCI 5832 Natural Language Processing
  • CSCI 5932 Deep Reinforcement Learning
  • CSCI 5214 Big Data Architecture
  • CSCI 5254 Convex Optimization
  • CSCI 5673 Distributed Systems
  • CSCI 5822 Probabilistic Models of Human and Machine Learning
  • CSCI 6414 Information Theory, Statistical Inference, and Experimental Design
  • CSCI 6712 Data-Centric Computer Vision
  • CSCI 7000 Special Topics in Computer Science (Neuro-Symbolic NLP)
  • CSCI 7000 Special Topics in Computer Science (NLP for Cultural Analytics)
  • CSCI 7000 Special Topics in Computer Science (Deep Language Understanding)
  • CSCI 7000 Special Topics in Computer Science (Systems for Machine Learning)
  • CSCI 7000 Special Topics in Computer Science (Geospatial and Statistical Machine Learning)
  • CSCI 7000 Special Topics in Computer Science (Vision Language Models for Robotics)
  • CSCI 7000 Special Topics in Computer Science (Physical Human-Robot Interaction)
  • CSCI 7314 Seminar on Algorithmic Economics and Machine Learning
  • APPM 5490 Theory of Machine Learning
  • APPM 5720 Open Topics in Applied Mathematics (Convex Optimization)
  • ECEN 5672 Digital Image Processing
  • ECEN 5772 Digital Video

Professional Credits (6 credits) - Required

Project courses 

Learn more about the Project Course Requirement

  • Students must complete both project courses from one project course sequence.
  • Students must earn grades of ‘B’ or better in both courses.
  • Students cannot count just one project course to the degree.