Artificial Intelligence, Post-Baccalaureate Certificate
Credential: Certificate
Field of Study: Artificial Intelligence
Program Overview
The post-baccalaurate certificate in artificial intelligence provides an official and transcriptable recognition of successful completion of study in the field of artificial intelligence. The certificate can be awarded to current CWRU graduate students and other students who apply to this certificate program.
Admission
Graduate students shall be admitted to the certificate program upon recommendation of the faculty of the CS program. Requirements for admission include a strong record of scholarship in a completed bachelor's degree program in artificial intelligence, computer science, or related areas, and fluency in written and spoken English.
The program requires applicants to have the background necessary to take advanced courses in Artificial Intelligence. Students should have knowledge equivalent to that in the courses:
| Code | Title | Credit Hours |
|---|---|---|
| CSDS 233 | Introduction to Data Structures | 4 |
| MATH 122 | Calculus for Science and Engineering II | 4 |
| or MATH 126 | Math and Calculus Applications for Life, Managerial, and Social Sci II | |
| MATH 307 | Linear Algebra | 3 |
| or MATH 201 | Introduction to Linear Algebra for Applications | |
| MATH 380 | Introduction to Probability | 3-6 |
| or STAT 301 & STAT 302 | Introduction to Probability for Statistics and Introduction to Statistical Inference | |
Learning Outcomes
- Analyze a complex computing problem and to apply principles of artificial intelligence, computing, and other relevant disciplines to identify solutions.
- Design, implement, and evaluate an artificial intelligence-based solution to meet a given set of requirements.
- Function effectively as a member or leader of a team engaged in activities appropriate to the discipline of artificial intelligence.
Graduate Policies
For graduate policies and procedures, please review the School of Graduate Studies section of the General Bulletin.
Program Requirements
Students must pass at least five courses from the following list. At most two courses can be at the 300-level.
| Code | Title | Credit Hours |
|---|---|---|
| CSDS 323 | Numerical Algorithms for Machine Learning | 3 |
| CSDS 330 | Introduction to Artificial Intelligence | 3 |
| CSDS 340 | Introduction to Machine Learning | 3 |
| CSDS 440 | Machine Learning | 3 |
| CSDS 443 | Algorithmic Fairness | 3 |
| CSDS 446 | Machine Learning on Graphs | 3 |
| CSDS 447 | Responsible AI Engineering | 3 |
| CSDS 452 | Causality and Machine Learning | 3 |
| CSDS 463 | AI in Medical Imaging | 3 |
| CSDS 464 | Computational Perception | 3 |
| CSDS 465 | Computer Vision | 3 |
| CSDS 491 | Probabilistic Models in AI | 3 |
| CSDS 496 | Sequential Decision Making | 3 |
| CSDS 497 | Foundations of Statistical Natural Language Processing | 3 |
| CSDS 570 | Deep Generative Models | 3 |