Minor in Artificial Intelligence Engineering

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.

Artificial Intelligence Engineering Minor enrolment form.

Enrolment

All U of T Engineering undergraduates (except students in the Engineering Science Machine Learning 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.

Requirements

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:

1. APS 360H: Artificial Intelligence Fundamentals

2. One of:

• CSC 263H1: Data Structures and Analysis
• ECE 345H1: Algorithms and Data Structures
• ECE 358H1: Foundations of Computing
• MIE 335H1: Algorithms & Numerical Methods

3. One of:

• CSC 384H1: Introduction to Artificial Intelligence
• MIE 36XH1: Introduction to Artificial Intelligence
• ROB 311H1: Artificial Intelligence

4. One of:

• 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. One or two of (ML/AI additional emphasis):

• CSC 401H1: Natural Language Processing
• CSC 420H1: Introduction to Image Understanding
• CSC 421H1: Neural Networks & Deep Learning
• CSC 485H1: Computational Linguistics
• CSC 486H1: Knowledge Representation and Reasoning
• CHE 507H1: Data-based Modelling for Prediction and Control
• ECE 368H1: Probabilistic Reasoning
• MIE 368H1: Analytics in Action (formerly MIE 465)
• MIE 451H1: Decision Support Systems
• MIE 457H1: Knowledge Modeling and Management
• MIE 566H1: Decision Analysis
• ROB 501H1: Computer Vision for Robotics (EngSci only)
• AI/ML-related capstone or project or Engineering Science 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 weight of the course – 0.5 FCE or 1.0 FCE).

6. If needed, 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
• BME 595H1: Medical Imaging
• CHE 322H1: Process Control
• CSC 343H1: Introduction to Databases
• CSC 412H1: Probabilistic Learning and Reasoning
• CSC 413H1: Neural Networks and Deep Learning
• ECE 311H1: Real-Time Computer Control
• ECE 344H1: Operating Systems
• ECE356H1: Introduction to Control Theory
• ECE 367H1: Matrix Algebra and Optimization
• ECE 419H1: Distributed Systems
• ECE 431H1: Digital Signal Processing
• ECE 444H1: Software Engineering (formerly CSC444)
• ECE 454H1: Computer Systems Programming
• ECE 470H1: Robot Modeling and Control
• ECE516H1: Intelligent Image Processing
• ECE 532H1: Digital Systems Design
• ECE 557H1: Linear Control Theory
• ECE 568H1: Computer Security
• MAT 336H1: Elements of Analysis
• MAT 389H1: Complex Analysis
• ROB 501H1: Computer Vision for Robotics
• STA 302H1: Methods of Data Analysis
• STA 410H1: Statistical Computation

Note: Engineering Science 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 design course can count for up to two elective in Requirement #5 IF the thesis or design course is strongly related to artificial intelligence. This requires approval from the Director of the minor.
  • Some departments may require students 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.