Graduate Degree

Artificial Intelligence - Master of Science

Program:
Artificial Intelligence - Master of Science
Plan Code:
ARFINT_MS
Program Level:
Graduate
Award Type:
Master of Science
College:
College of Engineering
Department:
Computer Science and Engineering


Excerpt from the official Academic Programs Catalog:

Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.


College of Engineering

Department of Computer Science and Engineering

Graduate Study
Artificial Intelligence - Master of Science

In addition to meeting the requirements of the university and of the College of Engineering, students must meet the requirements specified below.

Admission

For admission in the MS in Artificial Intelligence program, students must have a strong background in programming, mathematics and statistics. Students are expected to have a degree in a STEM discipline or a related field with experience in computer programming (CSE 231 and CSE 232) and algorithms (CSE 331), linear algebra (MTH 314), probability and statistics and multivariable calculus (MTH 234).

Requirements for Master of Science in Artificial Intelligence
Students must complete a minimum of 30 credit hours from the following:
1. The following required courses (12 credits):
CSE 807 Ethics of AI 3
CSE 840 Computational Foundations in Artificial Intelligence 3
CSE 841 Artificial Intelligence 3
MTH 800 Quantitative Foundations for Machine Learning 3
2. At least 3 of the following core courses:
CSE 802 Pattern Recognition and Analysis 3
CSE 803 Computer Vision 3
CSE 842 Natural Language Processing 3
CSE 847 Machine Learning 3
CSE 849 Deep Learning 3
CSE 851 Adversarial Machine Learning 3
MTH 801 Machine Learning Algorithms: Mathematical Analysis 3
3. At most 3 of the following elective courses (Student may choose to take additional core courses instead of elective courses):
CSE 404 Introduction Machine Learning 3
CSE 440 Introduction to Artificial Intelligence  3
CSE 848 Evolutionary Computing 3
CSE 850 Genetic Programming 3
CSE 881 Data Mining 3
CSE 830 Design and Theory of Algorithms 3
CSE 885 Artificial Neural Networks 3
STT 465 Bayesian Statistical Methods  3
STT 810 Mathematical Statistics for Data Scientists 3
STT 811 Applied Statistical Modeling for Data Scientists 3
STT 812 Statistical Learning and Data Analysis 3
STT 873 Statistical Learning and Data Mining  3
MTH 844 Projects in Industrial Mathematics 3
MTH 468 Predictive Analysis  3
MTH 483 Mathematical Machine Learning 3
CMSE 823 Numerical Linear Algebra/Numerical analysis 3
CMSE 831 Computational Optimization  3
ECE 855 Noncooperative game theory 3
4. At most 6 credits of capstone/thesis.
Students will register for independent study with a faculty member (CSE 890/899, CMSE 891/899, STT 890/899, MTH 890/899). To register for capstone/thesis, students must submit a proposal to the director of the program at least 30 days before the start of the semester and have it approved. 
5. If a student is interested in taking other courses (including special topics courses) in place of core or elective courses, they should get approval from the director at least 30 days before the start of the semester when the course is offered. 
6. Students cannot take more than 6 credits at 400-level. All 400-level courses must be approved by the director and are only intended for students who lack a background in the relevant area.