Artificial Intelligence (AI) has been a core strength of the School of Computing since 1984, when it introduced its groundbreaking Cognitive Science program. AI has played a leading role in the School's programs and research for over 40 years, so we're prepared to embrace the surge of interest.
Artificial Intelligence no longer feels like science fiction. With the introduction of ChatGPT at the end of 2022, suddenly the hypothetical became available to use.
If the hype or futuristic terminology around AI feels intimidating, please consider reading the School's audience-friendly AI Fact Sheet.
Research
The School of Computing proudly houses over a dozen faculty members that focus on a range of AI fields from machine learning, to evolutionary computing, to applications in medicine. You can find a list of the faculty members and related labs here.
Education
There are three AI-dedicated programs offered by the School:
- For Undergraduates,
- the sub-plan in Artificial Intelligence teaches how the human mind works and focuses on developing computational algorithms to create machine intelligence.
- the specialization in Cognitive Science further explores the interdisciplinary science of the mind and teaches you how to program intelligent computers.
- For Graduate students, the Vector-approved Field of Study in AI is designed for those who want to take AI-specific courses and complete a related dissertation.
Courses
The list of AI-related courses is growing constantly, and we currently offer a wide range of topics that primarily focus on either the theory behind, or applications of, Artificial Intelligence.
The ARIN courses are designed as electives for Computing and non-Computing students alike. The first-year ARIN courses have no prerequisites and the second-year courses have limited prerequisites.
Many of the School's other courses incorporate Generative AI in both the delivery and assessments of the material.
Core Undergraduate AI Courses
| Course Code | Course Title |
|---|---|
| COGS 100 | Introduction to Cognitive Science |
| COGS 201 | Cognition and Computation |
| CISC 271 | Linear Methods for Artificial Intelligence |
| CISC 352 | Artificial Intelligence |
| CISC 371 | Numerical Optimization for Artificial Intelligence |
| CISC 453 | Topics in Artificial Intelligence |
| CISC 455 | Evolutionary Optimization and Learning |
| CISC 473 | Deep Learning |
| CISC 474 | Reinforcement Learning |
Elective Undergraduate AI Courses
| Course Code | Course Title |
|---|---|
| ARIN 100 | Fundamentals of Artificial Intelligence |
| ARIN 101 | Artificial Intelligence in Society |
| ARIN 201 | Ethics and Fairness in Artificial Intelligence |
| ARIN 210 | Applications of Artificial Intelligence |
| CISC 251 | Data Analytics |
| CISC 330 | Computer-Integrated Surgery |
| CISC 351 | Advanced Data Analytics |
| CISC 451 | Topics in Data Analytics |
| CISC 452 | Neural and Genetic Computing |
| CISC 471 | Computational Biology |
| CISC 472 | Medical Informatics |
Graduate AI Courses
| Course Code | Course Title |
|---|---|
| CISC 813 | Automated Planning |
| CISC 843 | Mining Software Repositories |
| CISC 851 | Evolutionary Computing |
| CISC 855 | Nonlinear Optimization |
| CISC 856 | Reinforcement Learning |
| CISC 867 | Deep Learning |
| CISC 873 | Data Mining |
| CISC 874 | Neural and Cognitive Computing |
| CISC 881 | Medical Imaging and Machine Learning |
There are several initiatives underway with the sub-committee on AI in Education at the School of Computing, and we will update this space as they become a reality.