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
| Course | Title | Hours |
|---|---|---|
| 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
| Course | Title |
|---|---|
| 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
| Course | Title |
|---|---|
| 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
| Course | Title |
|---|---|
| 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:
| Course | SLU 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.