Artificial Intelligence (AI) and Machine learning (ML) have exploded in importance in recent years and garnered attention in a wide variety of application areas, including computer vision (e.g., image recognition), game playing (e.g., AlphaGo), autonomous driving, speech recognition, customer preference elicitation, bioinformatics (e.g., gene analysis) and others. While the topics may appear primarily to reside in the disciplines of computer engineering and computer science, the topics of AI and ML now apply to all disciplines of engineering, such as the projection of future road-traffic patterns, applications in industrial automation and robotic control, or the use of AI/ML drug discovery, to name just a few examples.
Join the AI Quercus Community Group for program updates, club events, scholarships and more!
Enrolment
All U of T Engineering undergraduates (except students in the Engineering Science Machine Intelligence Major) are eligible to participate in the Minor in Artificial Intelligence.
Please note that Engineering Science students in the Robotics Major will have to take additional courses due to the number of core courses that overlap with their degree program.
Artificial Intelligence Engineering minor enrolment form.
Requirements
The requirements for the Minor in Artificial Intelligence Engineering in the Faculty of Applied Science & Engineering are the successful completion of the following courses:
1. Mandatory:
- APS 360H1: Applied Fundamentals of Deep Learning
2. Choose one of the following:
- CSC 263H1: Data Structures & Analysis
- ECE 345H1: Algorithms & Data Structures
- ECE 358H1: Foundations of Computing (EngSci only)
- MIE 245H1: Data Structures and Algorithms
- MIE 335H1: Algorithms & Numerical Methods
3. Choose one of the following:
- CSC 384H1: Introduction to Artificial Intelligence
- MIE 369H1: Introduction to Artificial Intelligence
- ROB 311H1: Artificial Intelligence
4. Choose one of the following:
- CSC 311H1: Introduction to Machine Learning (formerly CSC 411)
- ECE 421H1: Introduction to Machine Learning
- MIE 424H1: Optimization in Machine Learning
- ROB 313H1: Introduction to Learning from Data
5. Chone one or two of (ML/AI additional emphasis):
- CHE 408H1: Data Analytics for Prediction, Control and Optimization of Chemical Processes
- CHE 507H1: Data-based Modelling for Prediction & Control
- CME 538H1: Intro to Data Science for Civil and Mineral Engineering
- CSC 401H1: Natural Language Computing
- CSC 412H1: Probabilistic Learning and Reasoning
- CSC 413H1: Neural Networks & Deep Learning
- CSC 420H1: Introduction to Image Understanding
- CSC 485H1: Computational Linguistics
- CSC 486H1: Knowledge Representation Reasoning
- ECE 368H1: Probabilistic Reasoning
- HPS 340H1: The Limits of Machine Intelligence (HSS)
- HPS 345H1: Quantifying the World: Ethical & Epistemic Implications of AI (HSS)
- HPS 346H1: Modifying & Optimizing Life: AI, Biology & Engineering (HSS)
- MIE 368H1: Analytics in Action (formerly MIE 465)
- MIE 451H1: Decision Support Systems
- MIE 457H1: Knowledge Modeling & Management
- MIE 509H1: AI for Social Good (HSS)
- MIE 524H1: Data Mining
- MIE 562H1: Scheduling
- MIE 566H1: Decision Making Under Uncertainty
- MIE 567H1: Dynamic & Distributed Decision Making
- MSE 403H1: Data Sciences & Analytics for Materials Engineers
- MSE 465H1: Application of Artificial Intelligence in Materials Design
- ROB 501H1: Computer Vision for Robotics (EngSci only)
- AI/ML-related capstone project or thesis (H or Y), with topic to be approved by the director of the minor. This capstone/thesis would count for either one or two credits (depending on the weight of the course – 0.5 FCE or 1.0 FCE).
6. If needed, choose one of the courses in the table below to bring the complement of courses to the minimum total of 3.0 FCE:
- AER 336H1: Scientific Computing (EngSci only)
- BME 595H1: Medical Imaging
- CHE 322H1: Process Control
- CSC 343H1: Introduction to Databases
- CSC 412H1: Probabilistic Learning & Reasoning
- ECE 344H1: Operating Systems
- ECE 353H1: Systems Software (EngSci only)
- ECE 356H1: Introduction to Control Theory (EngSci only)
- ECE 367H1: Matrix Algebra & Optimization
- ECE 411H1: Adaptive Control & Reinforcement Learning
- ECE 419H1: Distributed Systems
- ECE 431H1: Digital Signal Processing
- ECE 444H1: Software Engineering (formerly CSC444)
- ECE 454H1: Computer Systems Programming
- ECE 470H1: Robot Modeling & Control
- ECE516H1: Intelligent Image Processing
- ECE 532H1: Digital Systems Design
- ECE 557H1: Linear Control Theory (EngSci only)
- ECE 568H1: Computer Security
- MAT 336H1: Elements of Analysis
- MAT 389H1: Complex Analysis
- STA 302H1: Methods of Data Analysis I
- STA 410H1: Statistical Computation
Note: Engineering Science students enrolled in the Robotics Major will only be able to access the Artificial Intelligence in Engineering Minor with the permission of the Cross-Disciplinary Programs Office. The permission will be based on the selection of a suitable set of alternative courses.
Completion of the minor is subject to the following constraints:
- Of the six (half-year) courses required, one (half-year) course can also be a core course in a student’s program, if applicable.
- Either a thesis or capstone design course can count for up to two electives in Requirement #5 if the project is strongly related to artificial intelligence. This requires approval from the Director of the minor.
- Some departments may require students to select their electives from a pre-approved subset. Please contact your Academic Advisor for details.
- Arts & Science courses listed may be considered eligible electives for students taking the minor, subject to the student meeting any prerequisite requirements. Students must also seek the approval of their home program to ensure that they meet their degree requirements. In situations where these courses don't meet those of their home program, students can elect to take these as extra courses.