Academic Calendar - 2022

Western University Academic Calendar. - 2022

Courses


Course Numbering

0001-0999* Pre-University level introductory courses
1000-1999 Year 1 courses
2000-4999 Senior-level undergraduate courses
5000-5999 Professional Degree courses in Dentistry, Education, Law, Medicine and Theology (MTS, MDiv)
6000-6999 Courses offered by Continuing Studies
9000-9999 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.


Suffixes

no suffix 1.0 course not designated as an essay course
A 0.5 course offered in first term
B 0.5 course offered in second term
A/B 0.5 course offered in first and/or second term
E 1.0 essay course
F 0.5 essay course offered in first term
G 0.5 essay course offered in second term
F/G 0.5 essay course offered in first and/or second term
H 1.0 accelerated course (8 weeks)
J 1.0 accelerated course (6 weeks)
K 0.75 course
L 0.5 graduate course offered in summer term (May - August)
Q/R/S/T 0.25 course offered within a regular session
U 0.25 course offered in other than a regular session
W/X 1.0 accelerated course (full course offered in one term)
Y 0.5 course offered in other than a regular session
Z 0.5 essay course offered in other than a regular session

Glossary


Prerequisite

A course that must be successfully completed prior to registration for credit in the desired course.


Corequisite

A course that must be taken concurrently with (or prior to registration in) the desired course.


Antirequisite

Courses that overlap sufficiently in course content that both cannot be taken for credit.


Essay Courses

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).


Principal Courses

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.



Campus





Course Level






Course Type




Artificial Intelligence Systems Engineering


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.


Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.


Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B.

Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B.

Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B, AISE 3010A/B.

Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B, AISE 3010A/B.

Extra Information: 3 lecture hours/week, 2 lab hour/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B, AISE 3010A/B.

Extra Information: 1 lecture hour/week, 3 lab hours/week.

Course Weight: 0.50
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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.

Prerequisite(s): Data Science 3000A/B, AISE 3010A/B.

Extra Information: 1 lecture hour/week, 3 lab hours/week.

Course Weight: 0.50
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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.

Antirequisite(s): CBE 4497, CEE 4441, ECE 4416, MME 4499, Engineering Science 4499.

Prerequisite(s): Completion of fourth year of the Artificial Intelligence Systems Engineering program.

Extra Information: 6 lab hours/week, both terms.

Course Weight: 1.00
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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.

Antirequisite(s): CBE 4497, CEE 4441, ECE 4416, MME 4499, Engineering Science 4499.

Prerequisite(s): Completion of fourth year of the Artificial Intelligence Systems Engineering program.

Extra Information: 6 lab hours/week, both terms.

Course Weight: 1.00
More details
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