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 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.
All U of T Engineering undergraduates (except students in the Engineering Science Machine Learning Major) are eligible to participate in this minor. 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.
The requirements for the Minor in Artificial Intelligence Engineering in the Faculty of Applied Science and Engineering are the successful completion of the following courses:
Required Courses
| Fall Session - Year 1 | Lect. | Lab. | Tut. | Wgt. | |
|---|---|---|---|---|---|
| Required: | |||||
| APS360H1: Applied Fundamentals of Deep Learning | F/S | 3 | 1 | - | 0.5 |
| One of: | |||||
| CSC263H1: Data Structures and Analysis | F/S | 2 | - | 1 | 0.5 |
| ECE345H1: Algorithms and Data Structures | F/S | 3 | - | 2 | 0.5 |
| ECE358H1: Foundations of Computing | F | 3 | - | 1 | 0.5 |
| MIE245H1: Data Structures and Algorithms | S | 3 | 1 | 1 | 0.5 |
| One of: | |||||
| CSC384H1: Introduction to Artificial Intelligence | F/S | 2 | - | 1 | 0.5 |
| MIE369H1: Introduction to Artificial Intelligence | S | 3 | - | 2 | 0.5 |
| ROB311H1: Artificial Intelligence | S | 3 | - | 1 | 0.5 |
| One of: | |||||
| CSC311H1: Introduction to Machine Learning | F/S | 2 | - | 1 | 0.5 |
| ECE421H1: Introduction to Machine Learning | S | 3 | - | 2 | 0.5 |
| MIE424H1: Optimization in Machine Learning | S | 3 | 1 | - | 0.5 |
| ROB313H1: Introduction to Learning from Data | S | 3 | - | 2 | 0.5 |
| One or two of: | Lect. | Lab. | Tut. | Wgt. | |
| CHE408H1: Data Analytics for Prediction, Control and Optimization of Chemical Processes | 3 | 1 | - | 0.5 | |
| CHE507H1: Data-based Modelling for Prediction and Control | S | 3 | - | 1 | 0.5 |
| CME538H1: Intro to Data Science for Civil and Mineral Engineering | F | 3 | - | 1 | 0.5 |
| CSC401H1: Natural Language Computing | F/S/Y | 2 | - | 1 | 0.5 |
| CSC420H1: Introduction to Image Understanding | F | 3 | - | - | 0.5 |
| CSC412H1: Probabalistic Learning and Reasoning | S | 2 | 1 | 3 | 0.5 |
| CSC413H1: Neural Networks and Deep Learning | S | 2 | - | 1 | 0.5 |
| CSC485H1: Computational Linguistics | F/S | 3 | - | - | 0.5 |
| CSC486H1: Knowledge Representation and Reasoning | F/S/Y | 2 | - | 1 | 0.5 |
| ECE368H1: Probabilistic Reasoning | S | 3 | - | 1 | 0.5 |
| HPS340H1: The Limits of Machine Intelligence | S | 3 | 1 | 2 | 0.5 |
| HPS345H1: Quantifying the World & Epistemic Implications of AI | F/S/Y | 2 | - | - | 0.5 |
| HPS346H1: Modifying and Optimizing Life: AI, Biology & Engineering | F/S/Y | 2 | - | 1 | 0.5 |
| MIE368H1: Analytics in Action | F | 2 | 3 | 1 | 0.5 |
| MIE451H1: Decision Support Systems | F | 3 | 1 | 1 | 0.5 |
| MIE457H1: Knowledge Modelling and Management | S | 3 | 1 | 1 | 0.5 |
| MIE562H1: Scheduling | S | 3 | - | 2 | 0.5 |
| MIE566H1: Decision Making Under Uncertainty | F | 3 | - | 2 | 0.5 |
| MIE567H1: Dynamic & Distributed Decision Making | S | 3 | - | 2 | 0.5 |
| MIE524H1: Data Mining | F | 3 | 2 | - | 0.5 |
| MIE509H1: AI for Social Good | F | 3 | 2 | - | 0.5 |
| MSE403H1: Data Sciences and Analytics for Materials Engineers | S | 3 | 2 | - | 0.5 |
| MSE465H1: Application of Artificial Intelligence in Materials Design | F | 2 | 1 | - | 0.5 |
| ROB501H1: Computer Vision for Robotics | F | 3 | - | 1 | 0.5 |
| AI/ML-related capstone or thesis with Director's approval | F/S/Y | 0 |
| As needed to bring credit weight to 3.0: | Lect. | Lab. | Tut. | Wgt. | |
|---|---|---|---|---|---|
| AER336H1: Scientific Computing | S | 3 | - | 1 | 0.5 |
| BME595H1: Medical Imaging | F | 2 | 3 | 1 | 0.5 |
| CHE322H1: Process Control | S | 3 | - | 1 | 0.5 |
| CSC343H1: Introduction to Databases | F/S | 2 | - | 1 | 0.5 |
| CSC412H1: Probabilistic Learning and Reasoning | F/S | 3 | - | - | 0.5 |
| ECE344H1: Operating Systems | F/S | 3 | 3 | - | 0.5 |
| ECE353H1: Systems Software | S | 3 | 3 | - | 0.5 |
| ECE356H1: Introduction to Control Theory | S | 3 | 1.5 | 1 | 0.5 |
| ECE367H1: Matrix Algebra and Optimization | F | 3 | - | 2 | 0.5 |
| ECE411H1: Adaptive Control and Reinforcement Learning | S | 3 | 1.5 | 1 | 0.5 |
| ECE419H1: Distributed Systems | S | 3 | 1.5 | 1 | 0.5 |
| ECE431H1: Digital Signal Processing | F | 3 | 1.5 | 1 | 0.5 |
| ECE444H1: Software Engineering | F | 3 | 1.5 | 1 | 0.5 |
| ECE454H1: Computer Systems Programming | F | 3 | 3 | - | 0.5 |
| ECE470H1: Robot Modeling and Control | F/S | 3 | 1.5 | 1 | 0.5 |
| ECE516H1: Intelligent Image Processing | S | 3 | 3 | - | 0.5 |
| ECE532H1: Digital Systems Design | S | 3 | 3 | - | 0.5 |
| ECE557H1: Linear Control Theory | F | 3 | 1.5 | 1 | 0.5 |
| ECE568H1: Computer Security | F/S | 3 | 3 | - | 0.5 |
| MAT336H1 | F/S | 2 | - | 1 | 0.5 |
| MAT389H1: Complex Analysis | F | 3 | - | 1 | 0.5 |
| STA302H1 | F/S | 3 | - | - | 0.5 |
| STA410H1 | F | 3 | - | - | 0.5 |
NOTE:
- Robotics Major students in Engineering Science will only be able to access the 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.
- ROB313H1 and ROB501H1 may be only used towards the Minor by Engineering Science students.
- Either a thesis or design course may count for up to two electives IF the thesis or design course is strongly related to artificial intelligence. This requires approval by the Director of the Minor.