Technical Electives (TEs)

Computer Engineering and Electrical Engineering students must complete a total of eight technical electives (TEs) subject to the following conditions:

3B technical electives (3 courses)

Computer Engineering (CE)

Choose two from the following four courses:

  • ÌýComputer Architecture
  • Ìý°ä´Ç³¾±è¾±±ô±ð°ù²õ
  • ÌýDatabase Systems
  • ÌýComputer Networks

Electrical Engineering (EE)

Choose two from the following four courses:

  • ÌýDigital Signal Processing
  • ÌýElectronic Devices
  • ÌýPower Systems and Smart Grids
  • ÌýRadio Frequency and Microwave Circuits

Then both CE and EE students choose one additional course from this list:

Fourth-year TEs (3 courses, or up to 4)

Both CE and EE students must meet these requirements:

  • Three TEs chosen from - , or .

Non-ECE Engineering TE (1 course, orÌýup to 2)

NEWÌýbeginning with the class of 2024
Ìý

  • One TE (up to a maximum of two) must be from another Engineering program (aÌý"non-ECE Engineering TE").

Fourth-year TEs

Course/Title Term(s) generally offered

ÌýAlgorithm Design and Analysis

 Winter

ÌýCryptography and System Security

 Winter

ÌýWireless Communications

 Spring

ÌýAdvanced Topics in Networking

 Winter

ÌýImage Processing

 Winter

ÌýEmbedded Computer Systems

 Winter

ÌýRadio Frequency Integrated Devices and Circuits

 Winter

ÌýFabrication Technologies for Micro and Nano Devices

 Spring

ÌýIntegrated Analog Electronics

 Winter

ÌýIntegrated Digital Electronics

 Spring

ÌýSoftware Requirements Specification and Analysis

 Fall and Winter

ÌýSoftware Design and Architectures

 Spring and Winter

ÌýSoftware Testing, Quality Assurance, and Maintenance

 Winter

ÌýDistributed Computing

 Spring

ÌýEmbedded Software

 Spring

ÌýCo-operative and Adaptive Algorithms

 Spring and Fall

ÌýFundamentals of Computational Intelligence

 Winter

ÌýReinforcement Learning

 Spring

ÌýComputer Security

 Spring

ÌýProgramming for Performance

 Winter

ÌýElectrical Distribution Systems

 Spring

ÌýDesign and Applications of Power Electronic Converters

 Spring

ÌýHigh Voltage Engineering and Power System Protection

 Winter

ÌýPower Systems Analysis, Operations and Markets

 Winter

ÌýRadio and Wireless Systems

 Winter

ÌýRadio-Wave Systems

 Spring

ÌýDigital Control Systems

 Spring

ÌýRobot Dynamics and Control

 Spring

ÌýMultivariable Control Systems

 Winter
ECE 493_Topic 20 IoTÌýSignal Processing Spring
ECE 493_Topic 26 Social Robotics Winter
ÌýFoundations of Multi-agent Systems Winter

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Non-ECE Engineering TEs

A non-exhaustive list of non-ECE Engineering TEs is below. Please note that priority for these courses is for students from those programs so space can be limited. You should consider whether you are prepared sufficiently for these courses before requesting enrolment.
Please contact ECE Undergraduate Office if a course you're interested in taking as a TE is not listed.Ìý

Course/Title Term generally offered Prerequisites/restrictions
ÌýOptimization and Numerical Methods Fall Requires an override from BME as the prerequisite is 4A BME.
ÌýUltrasound in Medicine and Biology Fall Requires an override from BME. ECE 207 and ECE 375 are acceptable ECE replacements for the prerequisites listed in the calendar.
ÌýAdvanced Process Dynamics and Control Winter Require an override from CHE as the prereqÌýisÌý4A CHE.
Process Control Laboratory Winter Requires an override from CHE as the prereqÌýisÌý4A CHE
Fluid Mechanics 1 Fall, Winter, Spring Requires an override from ME as the prereq is 3A ME/MTE.
Energy Conversion Fall, Spring Requires an override from ME as the prereq is 4A ME/MTE.
ÌýRobot Manipulators: Kinematics, Dynamics, Control Winter Requires an override from ME as the prereq is 4A ME/MTE.
ÌýIntroduction to Optimization Fall, Winter, Spring No prerequisites restricting access
ÌýStochastic Models and Methods Winter Requires MSEÌý331, ECE 203 and ECE 306
ÌýProduction and Service Operations Management Fall, Winter Requires ECE 203 and ECEÌý306
ÌýAdvanced Optimization Techniques Winter Requires MSEÌý332
ÌýIntroduction to Machine Learning Winter Requires ECE 250 and ECE 307. Note that you cannot receive credit for more than one ofÌýECE 457B, MSEÌý446 and SYDE 522.
ÌýDecision Making Under Uncertainty Spring Requires ECE 203 and ECE 306
ÌýSearch EnginesÌý Fall RequiresÌýECE 203 and ECE 250​
ÌýAdvanced Machine Learning Winter Requires MSEÌý332 and ECE 457B
ÌýAutonomous Mobile Robots Fall No prerequisites restricting access
ÌýPhotonic Materials and Devices Fall Requires override from NE as prereq isÌý3B NE.
ÌýOptimization and Numerical Methods Fall Requires an override from SYDEÌýas the prerequisite is 4A SYDE.
ÌýFoundations of Artificial Intelligence Winter Requires override from SYDE. Note that you cannot receive credit for ECE 457A and SYDE 522 or ECE 457B and SYDE 522.
ÌýDesign Optimization Under Probabilistic Uncertainty Winter No prerequisites restricting access
ÌýInterface Design Winter Requires override from SYDE
ÌýBiomedical Measurement and Signal Processing Winter RequiresÌýBIOL 273 and override from SYDE
ÌýComputational Neuroscience Winter Requires override from SYDE
ÌýSimulating Neurobiological Systems Fall RequiresÌýBME 252 or SYDE 252
ÌýIntroduction to Pattern Recognition Winter Requires override from SYDE
ÌýImage Processing Fall Requires BME 252 or SYDE 252, and override from SYDE

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Pre-approved Technical Electives from outside Engineering

The courses listed belowÌýshow all non-EngineeringÌýcourses which have, in the past, been used as Technical Electives. You do not have to seek approval to have these counted as your allowed non-ECE technical electives.ÌýAccess to these courses is determined by the delivering department (Actuarial ScienceÌýfor ACTSC 446, Combinatorics & Optimization for CO 250 etc.) and space is not guaranteed for you.

Courses from other faculties, for example, the Faculty of Mathematics,Ìýdo not count as non-ECE Engineering technical electives. MSE courses designated as Complementary Studies Electives (CSEs) cannot count as technical electives.

  • Ìý-ÌýMathematical Models in Finance
  • Introduction to Optimization
  • ÌýIntroduction to Graph Theory
  • Introduction to Game Theory
  • Convex Optimization and Analysis
  • Continuous Optimization
  • Concurrent and Parallel Programming
  • User Interfaces
  • Principles of Programming Languages
  • Database Systems Implementation
  • Real-time Programming
  • Introduction to Machine Learning
  • Computational Vision
  • Statistical and Computational Foundations of Machine Learning

  • Introduction to Artificial Intelligence

  • Introduction to Computer Graphics

  • Stochastic Simulation Methods

  • Computational Statistics and Data Analysis

  • Computational Inference
  • Statistical Learning - Classification
  • Statistical Learning - Advanced Regression

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