M.S. Plan A

A Plan A degree is a traditional research-based Master's program that requires the student develop and complete a research project, and to successfully defend a written thesis. Refer to the Master's admissions guidelines for minimum requirements.

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MS in Electrical Engineering

The Plan A thesis document must be presented to the committee and an advertisement must be posted two weeks prior to the defense date. 

The following documentation is associated with the plan A option:

In addition to the above course work, the student must pass a final oral examination in defense of his or her thesis work. The presentation portion is advertised and is open to the public.

The student is highly encouraged to submit at least one paper to a peer-reviewed conference or journal describing the student's thesis work.

Plan A Credit Allocation (30 hours minimum - all at 4000 level minimum)

  • Minimum 16 Course Hrs in Electrical and Computer Engineering Course Work
  • Minimum 3 Course Hrs in Formal Course Work outside the Electrical and Computer Engineering Department
  • 7 Additional Formal Course Hrs in or out of the Electrical and Computer Engineering Department
  • 4 credits of MS thesis research
  • No more than 12 credit hours can be at the 4000 level
  • No more than 3 credit hours of independent study

See Course Catalog

MS in Computer Science

Each M.S. student will have a supervising committee of at least three members appointed. The committee will consist of at least two members of the computer science faculty and at least one non-COSC faculty member.

A total of at least 31 credit hours must be completed. The student must complete a minimum of 27 hours of courses, including the CORE & BREADTH REQUIREMENTS. At least 19 credit hours must be COSC courses. All COSC courses must be at the 5000 level. Courses from other departments, including no more than 6 hours of 4000-level courses, may be included with the approval of the supervising M.S. committee. 

Plan A students are required to formally defend their theses before their supervising committees. All defenses must be open and announced at least two weeks in advance.  The thesis must be distributed to the committee at least two weeks in advance of the defense. If the student does not pass the defense, the committee will instruct the student as to what needs to be accomplished (and by when) to pass.

Summary of Credit Requirements

  • Core: COSC 5110: 3
  • Breadth: theory course, AI course, two systems courses: 12
  • Additional courses: 12
  • Thesis/Dissertation (COSC 5960/5980): 4
  • Other credits (may include courses or COSC 5960/5980): 0
  • Total: 31

See Course Catalog

MS in Artificial Intelligence (AI)

The Plan A degree program consists of 24 hours of required coursework, two hours of seminar credits, and four hours of thesis research credits (COSC5960 or EE5960). Students must complete an accepted research thesis for the Plan A degree program approved by the student’s graduate committee. Students will take a total of 30 required credits.

Summary of Credit Requirements

Core Courses (9 Credits): Complete core courses from the core course list below:

  • COSC 5550: Introduction to Artificial Intelligence, Credits: 3.0
  • COSC 5555: Machine Learning, Credits: 3.0
  • EE 5410: Neural Networks, Credits: 3.0
  • MATH 4500, Matrix Theory, Credits: 3.0
  • STAT 5380, Bayesian Data Analysis, Credits: 3.0

Elective Courses (15 Credits):

  • EE 5440, Geometric and Deep Computer Vision, Credits: 3.0

  • COSC 5557, Practical Machine Learning, Credits: 3.0

  • EE 5885, Explainable AI, Credit: 3.0

  • EE 5885, AI for Multi-agent Systems, Credits: 3.0

  • EE 5885, Advancement in 3D Computer Vision, Credits: 3.0

  • EE 5885, Deep Reinforcement Learning and Control, Credits: 3.0

  • EE 5885, Cooperative Robotics, Credits: 3.0

  • EE 5885, AI and Game Theory for Machines

  • EE/COSC 5880 – Independent Study, Credits: 1.0 to 3.0

  • COSC 4800*, Introduction to Deep Learning, Credits: 3.0

  • EE/COSC 5885/5010, Introduction to LLMs

  • EE/COSC 5885*, Advanced Deep Learning, Credits: 3.0

  • EE/MATH 5885*, Mathematics for Machine Learning, Credits: 3.0

  • EE/COSC 5885/5010, Neurosymbolic AI

  • EE 5885, Directive-Based Parallel Programming

Seminar Courses: Credits: 2.0

  • COSC 5552, Advanced Topics in AI, Credits: 1.0
  • Course*, Credits: 1.0

*- New courses to be developed

Thesis Credits: EE/COSC 5960, Credits: 4.0

 

AI/ML Teaching Faculty:

1. Diksha Shukla, Associate Professor, EECS

2. Zejian Zhou, Assistant Professor, EECS

3. Shivanand Sheshappanavar, Assistant Professor, EECS

4. Chao Jiang, Associate Professor, EECS

5. Yaqoob Majeed, Assistant Professor, EECS

6. Dane R. Taylor, Assistant Professor, School of Computing

7. Lars Kotthoff, Associate Professor, EECS

8. John E. McInroy, Professor, EECS

9. Suresh Muknahallipatna, Professor, EECS