Fall Session – Year 3 | Lect. | Lab. | Tut. | Wgt. | |
---|---|---|---|---|---|

CHE374H1: Economic Analysis and Decision Making | F | 3 | - | 1 | 0.50 |

ECE355H1: Signal Analysis and Communication | F | 3 | - | 2 | 0.50 |

ECE358H1: Foundations of Computing | F | 3 | - | 1 | 0.50 |

ECE367H1: Matrix Algebra and Optimization | F | 3 | - | 2 | 0.50 |

ECE421H1: Introduction to Machine Learning | F | 3 | - | 2 | 0.50 |

ESC301H1: Engineering Science Option Seminar | Y | 1 | - | - | 0.25 |

Winter Session – Year 3 | Lect. | Lab. | Tut. | Wgt. | |
---|---|---|---|---|---|

ECE324H1: Machine Intelligence, Software and Neural Networks | S | 3 | - | 1 | 0.50 |

ECE353H1: Systems Software | S | 3 | 3 | - | 0.50 |

ECE368H1: Probabilistic Reasoning | S | 3 | - | 1 | 0.50 |

ESC301H1: Engineering Science Option Seminar | Y | 1 | - | - | 0.25 |

ROB311H1: Artificial Intelligence | S | 3 | - | 1 | 0.50 |

One (1) Technical Elective | S | - | - | - | 0.50 |

**YEAR 4 MACHINE INTELLIGENCE**

Year 4 | Lect. | Lab. | Tut. | Wgt. | |
---|---|---|---|---|---|

ESC499Y1: Thesis | Y | 3 | 2 | - | 1.00 |

MIE429H1: Machine Intelligence Capstone Design | F | - | - | 3 | 0.50 |

MIE451H1: Decision Support Systems | F | 3 | 1 | 1 | 0.50 |

Two (2) HSS/CS Electives | F/S/Y | - | - | - | 1.00 |

Three (3) Technical Electives | F/S | - | - | - | 1.50 |

One of: | |||||

ECE352H1: Computer Organization | F | 3 | 3 | - | 0.50 |

ECE419H1: Distributed Systems | S | 3 | 1.50 | 1 | 0.50 |

- Machine Intelligence Major students must complete 2.0 credits of Technical Electives, and 1.0 credit of Complementary Studies (CS) / Humanities and Social Sciences (HSS) electives in years 3 and 4. All students must fulfill the Faculty graduation requirement of 2.0 CS/HSS credits, at least 1.0 of which must be HSS. ESC203H1 is 0.5 HSS. Technical and CS/HSS Electives may be taken in any sequence.
- Some courses have limited enrolment. Availability of elective courses for timetabling purposes is not guaranteed. It is the student’s responsibility to ensure a conflict-free timetable. Technical Electives outside of the group of courses below must be approved in advance by the Division of Engineering Science.
- Students enrolled in the Machine Intelligence Major may take a maximum of four (4) 300- or 400- series courses in the Department of Computer Science (CSC).
- Students may take ECE352H1 in year 3 by moving CHE374H1 to year 4.

**Technical Electives**

Students may select their technical electives from any combination of the above groupings, which exist to help students with their course selection. New elective options will be considered on an annual basis, in particular as Machine Learning and related disciplines grow at the University of Toronto:

Technical Electives | Lect. | Lab. | Tut. | Wgt. | |
---|---|---|---|---|---|

Artificial Intelligence | |||||

CSC413H1: Neural Networks & Deep Learning | S | 2 | - | 1 | 0.50 |

CSC401H1: Natural Language Computing | S | 2 | - | 1 | 0.50 |

CSC420H1: Introduction to Image Understanding | F/S | 2 | 1 | - | 0.50 |

CSC485H1: Computational Linguistics | F | 3 | - | - | 0.50 |

CSC486H1: Knowledge Representation and Reasoning | S | 2 | - | 1 | 0.50 |

MIE424H1: Optimization in Machine Learning | S | 3 | 1 | - | 0.50 |

MIE457H1: Knowledge Modelling and Management | S | 3 | 1 | 1 | 0.50 |

MIE566H1: Decision Making Under Uncertainty | F | 3 | 2 | 2 | 0.50 |

MIE524H1: Data Mining | F | 3 | 2 | - | 0.50 |

Software | |||||

CSC343H1: Introduction to Databases | F/S | 2 | - | 1 | 0.50 |

ECE352H1: Computer Organization | F | 3 | 3 | - | 0.50 |

ECE444H1: Software Engineering | F | 3 | 1.50 | 1 | 0.50 |

ECE568H1: Computer Security | F/S | 3 | 3 | - | 0.50 |

ECE419H1: Distributed Systems | S | 3 | 1.50 | 1 | 0.50 |

ECE454H1: Computer Systems Programming | F | 3 | 3 | - | 0.50 |

Hardware | |||||

ECE411H1: Adaptive Control and Reinforcement Learning | S | 3 | 1.50 | 1 | 0.50 |

ECE470H1: Robot Modeling and Control | F/S | 3 | 1.50 | 1 | 0.50 |

ECE532H1: Digital Systems Design | S | 3 | 3 | - | 0.50 |

ROB501H1: Computer Vision for Robotics | F | 3 | - | 1 | 0.50 |

Mathematics and Modelling | |||||

AER336H1: Scientific Computing | S | 3 | - | 1 | 0.50 |

APM462H1: Nonlinear Optimization | F/S | 3 | - | - | 0.5 |

BME595H1: Medical Imaging | F | 2 | 3 | 1 | 0.50 |

ECE356H1: Introduction to Control Theory | S | 3 | 1.50 | 1 | 0.50 |

ECE431H1: Digital Signal Processing | F | 3 | 1.50 | 1 | 0.50 |

ECE557H1: Linear Control Theory | F | 3 | 1.50 | 1 | 0.50 |

MAT336H1: Elements of Analysis | S | 3 | - | - | 0.50 |

MAT389H1: Complex Analysis | F | 3 | - | 1 | 0.50 |

MIE376H1: Mathematical Programming (Optimization) | S | 3 | 2 | 1 | 0.50 |

STA302H1: Methods of Data Analysis I | F | 3 | - | - | 0.50 |

STA410H1: Statistical Computation | F | 3 | - | - | 0.50 |