Professional Degree courses in Dentistry, Education, Law, Medicine and Theology (MTS, MDiv)
Courses offered by Continuing Studies
Graduate Studies courses
* These courses are equivalent to pre-university introductory courses and may be counted for credit in the student's record, unless these courses were taken in a preliminary year. They may not be counted toward essay or breadth requirements, or used to meet modular admission requirements unless it is explicitly stated in the Senate-approved outline of the module.
1.0 course not designated as an essay course
0.5 course offered in first term
0.5 course offered in second term
0.5 course offered in first and/or second term
1.0 essay course
0.5 essay course offered in first term
0.5 essay course offered in second term
0.5 essay course offered in first and/or second term
1.0 accelerated course (8 weeks)
1.0 accelerated course (6 weeks)
0.5 graduate course offered in summer term (May - August)
0.25 course offered within a regular session
0.25 course offered in other than a regular session
1.0 accelerated course (full course offered in one term)
0.5 course offered in other than a regular session
0.5 essay course offered in other than a regular session
A course that must be successfully completed prior to registration for credit in the desired course.
A course that must be taken concurrently with (or prior to registration in) the desired course.
Courses that overlap sufficiently in course content that both cannot be taken for credit.
Many courses at Western have a significant writing component. To recognize student achievement, a number of such courses have been designated as essay courses and will be identified on the student's record (E essay full course; F/G/Z essay half-course).
A first year course that is listed by a department offering a module as a requirement for admission to the module. For admission to an Honours Specialization module or Double Major modules in an Honours Bachelor degree, at least 3.0 courses will be considered principal courses.
Survey of important computer algorithms and related data structures used in object-oriented software engineering. Design, performance analysis and implementation of such algorithms, stressing their practical use and performance certification of large software applications. Understand how to "seal" designs to guarantee performance goals and to ensure that error conditions are caught.
AISE 2251A/B is a group project course illustrating the design and implementation of software engineering design concepts. It covers integration with third-party applications and big data sources. Real-time and distributed systems, and architectural design will be briefly covered.
The course covers: 1) Introduction to data pipelines, distributed data management, and streamline data processing; 2) Data manipulation and data structure for big data; and 3) Design and implementation of an engineering group project illustrating the machine learning and data engineering concepts being taught.
This course explores the fundamental issues of fairness and bias in machine learning. In addition, the course explores many aspects of building ethical models while considering human bias and dataset awareness. Furthermore, the course will explore fundamental concepts involved in privacy and security of machine learning projects. Topics such as how to protect users from privacy violations while building useful transparent predictive models will be explored.
The focus is to teach database fundamentals required in the development and evolution of most software applications by providing a basic introduction to the principles of relational database management systems such as Entity-Relationship approach to data modeling, relational model of database management systems and the use of query languages.
This course covers: 1) Architecture of Cyber-Physical world. 2) Modelling of systems in continuous-time. Transfer functions. Stability. Feedback, and 3) Interface between physical and cyber worlds, including sensor, actuators, and sampling. Coordinate Transform, A/D and D/A conversion (electronics) and principles of data collection. Notion of information and control.
Sampling and reconstruction of signals, discrete signals and systems, difference equations and state-space models of digital systems, z-transform and system functions, finite impulse response (FIR) and infinite impulse response (IIR) systems, their mathematical description and frequency response, Fast Fourier transform, filter structures, basics of spectral analysis, data collection.
This course introduces deep learning models for time series data. In this course, the students will become familiar with sequence models and their engineering applications. The students are introduced to Recurrent Neural Networks (RNNs) and commonly - used variants such as Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs). In addition, the course introduces transformer architectures and their engineering applications. Students will get hands- on experience with Deep Learning from a series of practical engineering case-studies.
A large engineering project in the field of main engineering degree illustrating the machine learning and data engineering concepts through design and implementation. The course promotes team interaction in a professional setting.
Second large engineering project in the field of main engineering degree illustrating the machine learning and data engineering concepts through design and implementation. The course promotes team interaction in a professional setting. Individual students or project groups are carried out under the supervision of a faculty member. Progress reports and a final engineering report are prepared; each student must deliver a public lecture on the work performed.
Principles of computer networking architecture/layers and protocols, IoT network systems, protocols, security, and connections to cloud services with an emphasis on the ability to interface and collect data from things and move them securely through the internet to process in public and private data centers.
The course covers: 1) State-space control of systems using data processing algorithms. Adaptive algorithms. Implementation of Kalman filtering; 2) Use of ML in control of real-world Physical systems and Cyber systems.