Artificial Intelligence (AIG) Subplan Requirements

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 AIG subplan appears on the student’s transcript asÌýSpecialization in Artificial Intelligence; 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 - 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)Ìý³¦´Ç³Ü°ù²õ±ð²õ (9 credit hours)
      • One (1) course from Bin 1 list, with a grade of ‘B’ or higher
      • One (1) course from Bin 2 list, with a grade of ‘B’ or higher
      • One (1) course from Bin 3 list, with a grade of ‘B’ or higher
    • ±Ê°ù´ÇÂá±ð³¦³ÙÌýcourses (6 credit hours)
    • ElectiveÌý³¦´Ç³Ü°ù²õ±ð²õ (15 credit hours)
      • Any subplan courses that are non-CS will count towards the two (2) non-CS class limitation
      • ³§±ð±ðÌýMSCPS Degree Requirements for Electives eligibility
    • AIG SubplanÌý³¦´Ç³Ü°ù²õ±ð²õ
      • 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 AND Ìýone of their ÌýAIG 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) SubplanÌýCourseÌýOptions

  • CSCI 5202 -ÌýIntro to Robotics (,ÌýSyllabus)
  • CSCI 5214 -ÌýBig Data Architecture ()
  • CSCI 5254 -ÌýConvex Optimization (,ÌýSyllabus)
  • CSCI 5302 -ÌýAdvanced Robotics (,ÌýSyllabus)
  • CSCI 5322 -ÌýAlgorithmic Human-Robot Interaction (,ÌýSyllabus)
  • CSCI 5352 -ÌýNetwork Analysis and Modeling (,ÌýSyllabus)
  • ​CSCI 5434 -ÌýProbability for Computer Science (,ÌýSyllabus)
  • CSCI 5502Ìý-ÌýData Mining (,ÌýSyllabus)
  • CSCI 5622 -ÌýMachine Learning (,ÌýSyllabus)
  • CSCI 5673 -ÌýDistributed Systems (,ÌýSyllabus)
  • CSCI 5722 -ÌýComputer Vision ()
  • CSCI 5822Ìý-ÌýProbabilistic Models of Human and Machine Learning (,ÌýSyllabus)
  • CSCI 5832 -ÌýNatural Language Processing (,ÌýSyllabus)
  • CSCI 5922 -ÌýNeural Networks and Deep Learning (,ÌýSyllabus)
  • CSCI 5932 -ÌýDeep Reinforcement Learning (,ÌýSyllabus)
  • CSCI 5942 -ÌýAI Engineering (,ÌýSyllabus)
  • CSCI 7000 - Special Topics - Robot Perception
  • CSCI 7000 - Special Topics - Physical Human Robot Interaction
  • APPM 8500 - Statistics, Optimization, and Machine Learning Seminar