Computer Science, B.S. to Artificial Intelligence, M.S. Accelerated Program
Saint Louis University's computer science B.S. to artificial intelligence M.S. accelerated program allows a student to complete both the Bachelor of Science in Computer Science and the Master of Science in Artificial Intelligence at SLU in a shorter time period than if the degrees were pursued independently.
For additional information, see the catalog entries for the following SLU programs:
Students who wish to apply to this accelerated program should have completed all 2000-level coursework required of the computer science bachelor's program and have completed at least 75 credits at the time of application. At the time of application, students must have a cumulative GPA of at least 3.00 and a GPA of at least 3.00 in their computer science coursework.
Contact the graduate coordinator for more details.
Non-Course Requirements
All Science and Engineering B.A. and B.S. students must complete an exit interview/survey near the end of their bachelor's program.
Continuation Standards
Students must maintain a cumulative GPA of at least 3.00 and a GPA of at least 3.00 in their computer science coursework.
Students who drop below that GPA while in the accelerated program will be placed on a one-semester probationary period before being dismissed from the accelerated program.
Only grades of B or better in the graduate courses taken while an undergraduate can be applied to the master's degree.
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.
Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.
This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.
| Year One | ||
|---|---|---|
| Fall | Credits | |
| CSCI 10xx | Introduction to Computer Science | 3 |
| MATH 1510 | Calculus I | 4 |
| University Core and/or General Electives | 9 | |
| Credits | 16 | |
| Spring | ||
| CSCI 1300 | Introduction to Object-Oriented Programming | 4 |
| MATH 1510 | Calculus I | 4 |
| University Core and/or General Electives | 6 | |
| Credits | 14 | |
| Year Two | ||
| Fall | ||
| CSCI 2100 | Data Structures | 4 |
| CSCI 2500 | Computer Organization and Systems | 3 |
| MATH 1660 | Discrete Mathematics | 3 |
| Science I with lab | 4 | |
| PHIL 3050X | Computer Ethics | 3 |
| Credits | 17 | |
| Spring | ||
| CSCI 2300 | Object-Oriented Software Design | 3 |
| CSCI 2510 | Principles of Computing Systems | 3 |
| STAT 3850 | Foundation of Statistics | 3 |
| Science II with lab | 4 | |
| University Core and/or General Electives | 3 | |
| Credits | 16 | |
| Year Three | ||
| Fall | ||
| CSCI 3100 | Algorithms | 3 |
| Additional Mathematics/Statistics (2000+) | 3 | |
| Science or engineering | 3-4 | |
| University Core and/or General Electives | 6 | |
| Credits | 15-16 | |
| Spring | ||
| CSCI 3200 | Programming Languages | 3 |
| CSCI 3300 | Software Engineering | 3 |
| 5000-level version of CSCI Systems Elective | 3 | |
| Additional Mathematics/Statistics (2000+) | 3 | |
| University Core and/or General Electives | 3 | |
| Credits | 15 | |
| Year Four | ||
| Fall | ||
| CSCI 4961 | Capstone Project I | 2 |
| Critical course: CSCI 5750 | Introduction to Machine Learning | 3 |
| University Core and/or General Electives | 9 | |
| Credits | 14 | |
| Spring | ||
| CSCI 4962 | Capstone Project II | 2 |
| Critical course: CSCI 5740 | Introduction to Artificial Intelligence | 3 |
| University Core and/or General Electives | 9 | |
| Credits | 14 | |
| Year Five | ||
| Fall | ||
| CSCI 5030 | Principles of Software Development | 3 |
| Artificial Intelligence Foundations selection | 3 | |
| Artificial Intelligence Applications selection | 3 | |
| Artificial Intelligence Elective | 3 | |
| Credits | 12 | |
| Spring | ||
| Critical course: CSCI 5961 | Artificial Intelligence Capstone Project | 3 |
| Artificial Intelligence Foundation | 3 | |
| Or | ||
| Artificial Intelligence Application Course | ||
| CSCI 5xxx | General Elective a | 3 |
| Credits | 9 | |
| Total Credits | 142-143 | |
- a
Waiver replacement for CSCI 5050: Computing and Society.
Introduction to Computer Science
| Code | Title | Credits |
|---|---|---|
| Introduction to Computer Science: Principles | ||
| Introduction to Computer Science: Bioinformatics | ||
| Introduction to Computer Science: Cybersecurity | ||
| Introduction to Computer Science: Game Design | ||
| Introduction to Computer Science: Mobile Computing | ||
| Introduction to Computer Science: Multimedia | ||
| Introduction to Computer Science: Scientific Programming | ||
| Introduction to Computer Science: Taming Big Data | ||
| Introduction to Computer Science: World Wide Web | ||
| Introduction to Computer Science: Special Topics | ||
| With permission, a computing-intensive course from another discipline may be substituted. Examples of such courses include: | ||
| Biomedical Engineering Computing | ||
| Civil Engineering Computing | ||
| Foundation of Statistics | ||
Systems Courses
| Code | Title | Credits |
|---|---|---|
| Advanced Operating Systems | ||
| Computer Security | ||
| Computer Networks | ||
| Concurrent and Parallel Programming | ||
| Distributed Computing |
Program Notes
Thesis Option
A master's thesis is optional. Students completing a thesis should take six credits of Thesis Research (CSCI 5990) in lieu of the AI capstone project and either a foundations or applications selection.
Internship with Industry
Students may apply at most three credits of Internship with Industry (CSCI 5910) toward the degree requirements.