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Saint Louis University’s master’s program in artificial intelligence prepares students to apply artificial intelligence methods, both efficiently and ethically, in order to solve difficult problems and impact the well-being of society.

Highlights of the program are that:
Computer Science faculty provide students with depth of knowledge regarding models and technologies used to make advances in artificial intelligence and machine learning.
Faculty across the university guide students in applying AI/ML to specialized areas such as autonomous systems, bioinformatics, data science, health outcomes, image processing, and natural language processing.
The St. Louis region has a strong computer science ecosystem, including technical operations for many Fortune 500 companies, as well as a vibrant start-up community, including incubators such as CORTEX and T-REX, near to SLU’s campus.

Learning Outcomes

Graduates of this program will be able to:
1. Select the most appropriate choice among artificial intelligence methods for solving a given problem,
2. Design an experiment to evaluate the quality of a machine learning model and predict its accuracy in a solution environment,
3. Apply techniques from artificial intelligence to solve complex problems in an application domain,
4. Design and implement a software solution that meets a given set of computing requirements,
5. Make informed and ethical decisions regarding the impact of artificial intelligence technologies,
6. Assess literature and technical documents in the fields of artificial intelligence and machine learning,
7. Effectively communicate methods and results to both professional and general audiences in both oral and written form.

Degree Requirements

 Artificial Intelligence Applications course3 Artificial Intelligence Electives9

CourseTitleHours
CSCI 5030 Principles of Software Development 3
CSCI 5050 Computing and Society 3
CSCI 5740 Introduction to Artificial Intelligence 3
CSCI 5750 Introduction to Machine Learning 3
CSCI 5961 Artificial Intelligence Capstone 3
  Artificial Intelligence Foundations course 3
  Artificial Intelligence Applications course 3
  Additional Artificial Intelligence electives 9
Total    30

Artificial Intelligence Foundations courses

These courses have a primary focus on techniques in artificial intelligence and/or machine learning that have wide application to a variety of domain areas. Students must take at least one such course. The full list of approved courses is maintained by the Computer Science Department and includes

CourseTitle
CSCI 5730 Evolutionary Computation
CSCI 5745 Advanced Techniques in Artificial Intelligence
CSCI 5760 Deep Learning
STAT 5087 Applied Regression
STAT 5088 Bayesian Statistics and Statistical Computing

Artificial Intelligence Applications courses

These courses explore how tools or techniques from artificial intelligence are applied to solve problems in a specific domain area. Students must take at least one such course. The full list of approved courses is maintained by the Computer Science Department and includes

CourseTitle
BCB 5350 Machine Learning for Bioinformatics
BME 5150 Brain Computer Interface
CSCI 5070 Algorithmic Fairness
CSCI 5755 Natural Language Processing
CSCI 5570 Learning and Inference in Networking
CSCI 5830 Computer Vision
GIS 5092 Machine Learning for GIS and Remote Sensing
HDS 5330 Predictive Modeling and Machine Learning

Artificial Intelligence Supporting courses

AI Supporting courses must serve one of three purposes: (1) provide knowledge in a specific domain area that prepares students to apply artificial intelligence or machine learning to solve problems in that particular domain; (2) provide richer foundational knowledge in a supporting area (e.g. algorithms, statistics) that prepares students to understand, enhance, or implement artificial intelligence techniques; (3) provide exploration of the broader impacts of artificial intelligence. Students may apply at most 6 credit hours of such courses to the degree. The full list of approved courses is maintained by the Computer Science Department and includes

CourseTitle
AENG 5800 Autonomous Systems Design
BCB 5200 Introduction to Bioinformatics I
BCB 5250 Introduction to Bioinformatics II
CSCI 5100 Algorithms
CSCI 5710 Databases
CSCI 5850 High-Performance Computing
ECE 5120 Modern Control Theory
ECE 5153 Image Processing
ECE 5226 Mobile Robotics
GIS 5060 Geospatial Methods in Environmental Studies
LAW 8235 Information Privacy Law
PSY 510 Memory & Cognition
SOC 5670 Spatial Demography: Applied Statistics for Spatial Data
STAT 5084 Time Series

Artificial Intelligence Elective courses

The remaining electives can be taken from any of the Foundations, Applications, or Supporting categories, yet with a limit of at most six credit hours of Supporting courses.

Continuation Standards

Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.

Sample Schedule

While students' schedules depend upon their interests, incoming credits/experience, and consultation with faculty mentors, the following shows a typical schedule for completing the MS in Artificial Intelligence.

Semester One (9 credit hours)
CSCI 5030: Principles of Software Development 3
CSCI 5740: Introduction to Artificial Intelligence 3
CSCI 5750: Machine Learning 3
Semester Two (12 credit hours)
Artificial Intelligence Foundations selection 3
Artificial Intelligence Applications selection 3
Artificial Intelligence Elective 3
Artificial Intelligence Elective 3
Semester Three (9 credits)
CSCI 5050: Computing and Society 3
CSCI 5961: Artificial Intelligence Capstone 3
Artificial Intelligence Elective 3

Admissions

A bachelor's degree in a science, technology, engineering or math major (STEM) is typical. Most successful applicants have an undergraduate grade point average of 3.00 or better on a 4.00 scale. Applicants should have evidence of strong computational skills (generally through prior coursework in programming and data structures) as well as evidence of strong mathematical skills (generally through prior coursework in calculus and statistics). Typical prerequisite coursework would include:

CourseSLU Equivalent
Intro. to Programming CSCI 1300
Data Structures CSCI 2100
Object-Oriented Design CSCI 2300
Calculus I & II MATH 1510/1520
Intro. to Statistics MATH 3810 or 3850

Applicants must provide transcripts for all previous education, a resume, professional goal statement, and at least one letter of recommendation. Additional letters of recommendation and GRE general test scores are recommended.

See further instructions for online applications.

International students must also provide a declaration of financial support packet and demonstrate English language proficiency, either by submitting their TOEFL or IELTS results or by completion of English Level 6 with no grades below B. Minimum scores for direct admission are TOEFL IBT 80, or IELTS 6.5.

Students with the appropriate academic background but lacking sufficient English language proficiency might consider one-semester or two-semester graduate pathways administered by a SLU INTO partnership.

Financial Aid

The program offers a variety of opportunities for financial support through a combination of university-funded and research-funded Graduate Assistantships that include both tuition and a stipend, and through some full or partial tuition scholarships. All candidates who apply to the graduate program by the stated deadline will automatically be considered for financial support in addition to admission to the program.