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

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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.Ìý

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

The subplan appears on the student’s transcript asÌýspecialization.ÌýThe 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 - MSCPS with AIG Subplan

  • The MSCPS with AIG subplan requires 30 credit hours of graduate coursework according toÌýMSCPS degree requirements and the requirements for the AIG subplan:
    • Breadth (BIN)Ìýcourses (9 credit hours)
      • Computer Science courses are listed in the three different breadth bins.
      • Students must earn a B or better (not a B-) in at leastÌýone 5000-level course (not 6000 or higher) from each of theÌýthreeÌýbins.
      • By petition to the Graduate Committee, similar transferred graduate work done elsewhere may be used to satisfy part of this requirement.Ìý
    • 6 hours -ÌýProject courses
      • 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.
    • 15 hours - Elective courses
      • You may take up to two, approved, non-CS courses.
      • Any non-Computer Science courses that can count towards a subplan will count against the two maximum allowable non-CS classes.
    • AIG Subplan courses
      • Must complete at leastÌý12 credit hours of eligible AIG subplan courses with a grade of ‘B’ or better

Counting courses for the AIG subplan

Students can satisfy subplan requirements by counting eligible BIN and/or Elective courses towards the AIG subplan requirements. This means:

  • Students may count an eligible course towards BOTH a Breadth requirement AND a subplan requirement
  • If the student does not need to count an eligible course towards the Breadth requirement, the course can count towards BOTH an Elective requirement AND a subplan requirement
  • For example, if a student earned a ‘B’ or higher in CSCI 5832:
    • …and the Bin 2 requirement was incomplete - CSCI 5832 counts towards BOTH their Bin 2 requirement ANDÌý one of theirÌý AIG subplan courses
    • …and the Bin 2 requirement was completed - CSCI 5832 counts towards BOTH their Electives requirement AND as one of their AIG subplan courses

Artificial Intelligence (AIG) Subplan Course Options

  • CSCI 5202 - Intro to Robotics (,ÌýSyllabus)
  • CSCI 5214 - Big Data Architecture ()
  • CSCI 5254 - Convex Optimization (,ÌýSyllabus)
  • CSCI 5302 - Advanced Robotics (,ÌýSyllabus)
  • CSCI 5322 - Algorithmic Human-Robot Interaction (,ÌýSyllabus)
  • CSCI 5352 - Network Analysis and Modeling (,ÌýSyllabus)
  • ​CSCI 5434 - Probability for Computer Science (,ÌýSyllabus)
  • CSCI 5502 - Data Mining (,ÌýSyllabus)
  • CSCI 5622 - Machine Learning (,ÌýSyllabus)
  • CSCI 5673 - Distributed Systems (,ÌýSyllabus)
  • CSCI 5722 - Computer Vision ()
  • CSCI 5822 - Probabilistic Models of Human and Machine Learning (,ÌýSyllabus)
  • CSCI 5832 - Natural Language Processing (,ÌýSyllabus)
  • CSCI 5922 - Neural Networks and Deep Learning (,ÌýSyllabus)
  • CSCI 5932 - Deep Reinforcement Learning (,ÌýSyllabus)
  • CSCI 5942 - AI Engineering (,ÌýSyllabus)
  • CSCI 7000 - Special Topics - Robot Perception
  • CSCI 7000 - Special Topics - Physical Human Robot Interaction
  • APPM 8500 - Statistics, Optimization, and Machine Learning