MACHINE INTELLIGENCE (AEESCBASEL)

YEAR 3 MACHINE INTELLIGENCE

Fall Session – Year 3   Lect. Lab. Tut. Wgt.
CHE374H1: Economic Analysis and Decision Making F 3 - 1 0.50
ECE324H1: Introduction to Machine Intelligence 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
ESC301H1: Engineering Science Option Seminar Y 1 - - 0.25
Winter Session – Year 3   Lect. Lab. Tut. Wgt.
ECE353H1: Systems Software S 3 3 - 0.50
ECE368H1: Probabilistic Reasoning S 3 - 1 0.50
ECE421H1: Introduction to Machine Learning S 3 - 2 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 - - 5 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:          
ECE419H1: Distributed Systems S 3 1.50 1 0.50
ECE454H1: Computer Systems Programming F 3 3 - 0.50
  1. 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.
  2. 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.
  3. 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).
  4. Students may take Computer Systems Programming ( ECE454H1) in year 3 by moving Economic Analysis and Decision Making ( 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          
CSC310H1: Information Theory F 2 - 1 0.50
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 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 Analysis F 3 - 2 0.50
Software          
CSC343H1: Introduction to Databases F/S 2 - 1 0.50
ECE444H1: Software Engineering F 3 1.50 1 0.50
ECE352H1: Computer Organization F 3 3 - 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: Real-Time Computer Control 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
BME595H1: Medical Imaging S 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
STA302H1: Methods of Data Analysis I F 3 - - 0.50
STA410H1: Statistical Computation F 3 - - 0.50