Computer Science, B.A. to Artificial Intelligence, M.S. Accelerated Program
This program allows a student to complete, in an accelerated fashion, both the Bachelor of Arts in Computer Science and the Master of Science in Artificial Intelligence at Saint Louis University.
Admission Requirements
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.
Program Requirements
Students must maintain a cumulative GPA of at least 3.00 and a GPA of at least 3.00 in their computer science coursework. Only grades of B or better in the graduate courses taken while an undergraduate can be applied to the master's degree. 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.
Students can take up to 15 credits of graduate coursework that will count for both the B.A. and M.S. Courses and credits included are below.
| Undergraduate Program Requirement | Met by Graduate Course |
|---|---|
| 3-credit Systems Elective at the 3000-level | Systems Elective at the 5000-level |
| CS Elective at the 3000-level or above | CSCI 5750 |
| CS Elective at the 3000-level or above | CSCI 5740 |
| 6 credits of CSCI Electives | 6 credits of AI Electives |
This roadmap is just one example of a semester-by-semester plan of study for this program. There are other plans students can and do take. The plan of study for each particular student is established in consultation with each student’s academic advisor; this roadmap does not replace academic advising appointments.
Roadmap notes:
- This Roadmap assumes full-time enrollment unless otherwise noted.
- Courses/Milestones marked with an “!” are critical and must be completed in the semester listed in the Roadmap to ensure a timely graduation.
- Course availability and sequencing are subject to change.
| Year One | ||
|---|---|---|
| Fall | Credits | |
| CSCI 10xx | Introduction to Computer Science † | 3 |
| MATH 1660 | Discrete Mathematics | 3 |
| University Core and/or General Electives | 9 | |
| Credits | 15 | |
| 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 1520 | Calculus II | 4 |
| University Core and/or General Electives | 6 | |
| Credits | 17 | |
| Spring | ||
| CSCI 2300 | Object-Oriented Software Design | 3 |
| CSCI 2510 | Principles of Computing Systems | 3 |
| MATH 3850 | Foundation of Statistics | 3 |
| University Core and/or General Electives | 6 | |
| Credits | 15 | |
| Year Three | ||
| Fall | ||
| CSCI 3100 | Algorithms | 3 |
| University Core and/or General Electives | 12 | |
| Credits | 15 | |
| Spring | ||
| 5000-level version of CSCI Systems Elective | 3 | |
| PHIL 3050X | Computer Ethics | 3 |
| University Core and/or General Electives | 9 | |
| 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 | 6 | |
| Graduate AI Elective | 3 | |
| Credits | 14 | |
| Spring | ||
| CSCI 4962 | Capstone Project II | 2 |
| University Core and/or General Electives | 6 | |
| Critical course: CSCI 5740 | Introduction to Artificial Intelligence | 3 |
| Graduate AI Elective | 3 | |
| Credits | 14 | |
| Year Five | ||
| Fall | ||
| CSCI 5030 | Principles of Software Development | 3 |
| Artificial Intelligence Principles course | 3 | |
| Artificial Intelligence Applications course | 3 | |
| Credits | 9 | |
| Spring | ||
| Critical course: CSCI 5961 | Artificial Intelligence Capstone Project | 3 |
| CSCI 5xxx | General Elective a | 3 |
| Credits | 6 | |
| Total Credits | 134 | |
- 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 as long as it is not already fulfilling another requirement. Examples of such courses include: | ||
| Biomedical Engineering Computing | ||
| Civil Engineering Computing | ||
| Foundation of Statistics | ||
Systems
| Code | Title | Credits |
|---|---|---|
| Operating Systems | ||
| Computer Security | ||
| Computer Networks | ||
| Concurrent and Parallel Programming | ||
| Distributed Computing |
Program Notes
CSCI 5050 Computing and Society (3 cr) requirement will be waived for students who took Computer Ethics as an undergraduate. These hours would become an additional graduate elective.
Thesis Option
A master's thesis is optional. Students completing a thesis should take six credits of CSCI 5990 Thesis Research (0-6 cr) as part of the elective requirements.
Internship with Industry
Students may apply at most three credits of CSCI 5910 Internship with Industry (1-3 cr) toward the degree requirements.
Closely Related Disciplines
With approval, students may include up to six credits of elective graduate coursework in closely related disciplines (e.g., mathematics and statistics, bioinformatics and computational biology, electrical and computer engineering).
Students at Saint Louis University-Madrid can complete the first three or four years of their accelerated program at SLU-Madrid and then finish their M.S. at SLU-St. Louis.
Depending on the format of their M.S., students will transition before their fourth or fifth year of study. Options are listed below.
Option A: 3+2 Pathway (Thesis)
- Years 1-3: Students complete foundational coursework in Madrid.
- Years 4-5: Final year of B.A. and M.S. coursework/research completed in St. Louis. Students begin capstone research work in St. Louis in year four, which transitions into a formal M.S. thesis project and completion in year five.
Option B: 4+1 Research-Linked Pathway (Thesis)
- Years 1-4: Full B.A. completion in Madrid, including a senior capstone research project initiated under the guidance of SLU-Madrid faculty, in collaboration with St. Louis faculty. Students also complete the first 15 credits of M.S.-level coursework (5xxx level; potentially through online courses when available from STL campus).
- Year 5: Students matriculate fully into the M.S. program to continue and complete their research thesis and the remaining 15 credits of M.S. coursework in St. Louis.
Option C: 4+1 Coursework-Based Pathway (Nonthesis)
- Years 1-4: Students complete B.A. in Madrid and the first 15 credits of their MS coursework (5xxx level; potentially through online courses when available from STL campus).
- Year 5: Remaining 15 credits of M.S. coursework is completed in St. Louis.
For more information about computer science programs, please call 314-977-6667 or email cs@slu.edu.