Academic Calendar - 2023
Western University Academic Calendar. - 2023
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
Return to top
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
Return to top
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
Return to top
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
Return to top
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
Return to top
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
Return to top
Artificial Intelligence Systems Engineering 4020A/B ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN I
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
Return to top
Artificial Intelligence Systems Engineering 4020A/B ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN I
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
Return to top
Artificial Intelligence Systems Engineering 4050 ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN II
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.
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
Return to top
Artificial Intelligence Systems Engineering 4050 ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN II
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.
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
Return to top
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
Return to top
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
Return to top
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
Return to top
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
Return to top
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
Return to top
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
Return to top
Artificial Intelligence Systems Engineering 4020A/B ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN I
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
Return to top
Artificial Intelligence Systems Engineering 4020A/B ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN I
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
Return to top
Artificial Intelligence Systems Engineering 4050 ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN II
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.
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
Return to top
Artificial Intelligence Systems Engineering 4050 ARTIFICIAL INTELLIGENCE SYSTEMS ENGINEERING DESIGN II
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.
Prerequisite(s): Completion of fourth year of the Artificial Intelligence Systems Engineering program.
Extra Information: 6 lab hours/week, both terms.