AI Courses at McGill

Artificial Intelligence was once an area of technology that was considered advanced and inaccessible, often described by an ivory tower with many layers of complex computational and mathematical techniques. Today, being fluent in machine learning has become an expectation in the tech industry, and has become a skill that is required regardless of specialization due to its ubiquitous and versatile applications.

While a graduate degree is still required for research in artificial intelligence, most foundational skills and knowledge can be developed at the undergraduate level. In fact, those who wait until the graduate level to start learning about artificial intelligence have already fallen behind due to the field’s remarkable pace of development. Not only is it mandatory to have some prior experience in artificial intelligence to get into a specialized graduate program, even employers expect a certain level of fluency in machine learning and artificial intelligence of their undergraduate employees.

Despite its high demand, there are still very few schools in North America that offer specialized programs for artificial intelligence at the undergraduate level. McGill currently has many graduate programs in artificial intelligence, but the only way for undergraduates to learn about the industry through their education is to mix and match relevant classes and hope that it will also count towards credits in their degree. Many of the executive members on the McGill AI Society have gone through this problem and have realized the struggle behind it. Thus, we have compiled a list of courses offered to undergraduate students at McGill which relate to artificial intelligence.

Note:

* Recommended courses
** Mandatory courses

Essential Math Courses

Artificial Intelligence, at its core, is mathematics. Therefore, our list begins with the essential math courses that should be understood before diving into any sort of specialized concepts. For those in their earlier years of study, we highly recommend taking some of these math classes as they are essential for understanding most topics in the AI industry.

• Calculus I to Calculus IV (Course codes vary by program)
• ECSE 205 — Probability & Statistics (Engineering Only) **
• MATH 223 — Linear Algebra **
• MATH 271 — Linear Algebra & PDEs (Engineering Only)
• MATH 323 — Probability **
• MATH 324 — Statistics **
• MATH 240 — Discrete Structures I

Advanced Math Courses

For those interested in furthering strengthening their foundations in mathematics in order to better prepare themselves for artificial intelligence research, the following is a list of additional math classes at McGill which will make your life even easier as you dive deeper into the field.

• MATH 423/533 — Regression and Analysis of Variance
• MATH 447/547 — Stochastic Processes
• MATH 523 — Generalized Linear Models
• MATH 525 — Sampling Theory and Applications
• MATH 556 — Mathematical Statistics 1 *
• MATH 557 — Mathematical Statistics 2
• MATH 560/ECSE 507 — Optimization and Optimal Control *
• MATH 680 — Computationally Intensive Statistics

Software and Programming Courses

While AI research is highly focused on math, software and programming is still involved — especially for those applying AI in the industry. For those who aren’t already studying a software specialized degree, the following are some recommended programming courses for you to learn the essentials.

• COMP 202— Foundations of Programming **
• COMP 206 — Introduction to Software Systems
• COMP 250 — Introduction to Computer Science **
• COMP 251 — Algorithms and Data Structures
• COMP 310 — Operating Systems
• COMP 421 — Database Systems

Specialized AI Courses

Aside from mathematics, the rest of our list includes artificial intelligence courses from specialized fields, which we recommend you select from based on your specific interests.

• ECSE 415 / COMP 558 — Computer Vision *
• COMP 424 / ECSE 526 — Artificial Intelligence *
• ECSE 508 — Multi-Agent Systems
• ECSE 529 — Computer and Biological Vision
• COMP 550 — Natural Language Processing *
• COMP 551 — Applied Machine Learning **
• COMP 598 — Mathematical Foundations in Machine Learning
• ECSE 608 / COMP 652 — Machine Learning
• COMP 762 — Reinforcement Learning

And that’s the end of our list! Sooner or later, it is likely that McGill and other universities will create a more formal track for those interested in artificial intelligence — but for now, we hope that this list will help you plan your degree to benefit your future in industry. If you know of any other courses, feel free to make a comment below. If you have any further questions regarding artificial intelligence courses at McGill, feel free to reach out to myself or any of our executive members at the McGill AI Society!

John Wu
Co-President
McGill AI Society