| Instructor(s) | Ghassem Tofighi [Coordinator] Office: online - by appointment Phone: TBA Email: gtofighi@torontomu.ca Office Hours: online - by appointment | ||||||||||||||
| Calendar Description | Machine learning and pattern classification are fundamental blocks in the design of an intelligent system. This course will introduce fundamentals of machine learning and pattern classification concepts, theories, and algorithms. Topics covered include: Bayesian decision theory, linear discriminant functions, multilayer neural networks, classifier evaluation, and an introduction to unsupervised clustering/grouping, and other state-of-the-art machine learning and AI algorithms. | ||||||||||||||
| Prerequisites | ELE 532 or MEC 733 | ||||||||||||||
| Antirequisites | None | ||||||||||||||
| Corerequisites | None | ||||||||||||||
| Compulsory Text(s): |
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| Reference Text(s): |
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| Learning Objectives (Indicators) | At the end of this course, the successful student will be able to:
NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB). | ||||||||||||||
| Course Organization | 3.0 hours of lecture per week for 13 weeks | ||||||||||||||
| Teaching Assistants | TBA | ||||||||||||||
| Course Evaluation |
Note: In order for a student to pass a course, a minimum overall course mark of 50% must be obtained. In addition, for courses that have both "Theory and Laboratory" components, the student must pass the Laboratory and Theory portions separately by achieving a minimum of 50% in the combined Laboratory components and 50% in the combined Theory components. Please refer to the "Course Evaluation" section above for details on the Theory and Laboratory components (if applicable). | ||||||||||||||
| Examinations | Midterm exam, two hours, closed book. Final exam, during exam period, three hours, closed-book. | ||||||||||||||
| Other Evaluation Information | Laboratories There are 4 practical assignments in this course. These are to be done in partners and handed in electronically online. These assignments are more like mini-projects and are NOT meant to be done/completed in the assigned lab hours. They are to be done primarily outside lab and lecture hours. The assigned lab hours are available for you to make use of as you see fit and will also be the best time to get direct help from the TA on these assignments. The assignments will consist of theoretical and practical parts and will require use of Python programming language. | ||||||||||||||
| Teaching Methods | The course is delivered in-person/online or hybrid. All communication is online. All course materials are provided on the course web. | ||||||||||||||
| Other Information | Some practical problems and solutions will be on the course web page as a study guide. You are strongly recommended to attempt to solve the problems on your own without looking at the solutions first. It is your responsibility to check the course web and download the materials. If you have any question about a problem or its respective solution, please consult the course instructor or the teaching assistant during their consulting hours. | ||||||||||||||
Week | Hours | Chapters / | Topic, description |
|---|---|---|---|
1 | 6 | 1- Introduction to the concept, History and application of Intelligent Systems | |
2 | 6 | 1- Artificial Neural Networks (ANN) and Nonlinear Regression | |
3 | 6 | 1- Evolutionary Computation | |
4 | 6 | 1- Midterm Exam | |
5 | 6 | 1- Unsupervised Learning and Clustering | |
6 | 6 | 1- Deep learning and Convolutional Neural Networks (CNN) | |
7 | 6 | 1- Challenges on the Implementation of Intelligent Systems |
Week | L/T/A | Description |
|---|---|---|
1 | Lab 0 | Lab Assignment 0: Intro to Python for Machine Learning |
2 | Lab 1 | Lab Assignment 1: Regression |
3 | Lab 2 | Lab Assignment 2: Multilayer Neural Network |
5 | Lab 3 | Lab Assignment 3: Classification |
6 | Lab 4 | Lab Assignment 4: Unsupervised Learning |
Students are reminded that they are required to adhere to all relevant university policies found in their online course shell in D2L and/or on the Senate website
Refer to the Departmental FAQ page for furhter information on common questions.
You can submit an Academic Consideration Request when an extenuating circumstance has occurred that has significantly impacted your ability to fulfill an academic requirement. You may always visit the Senate website and select the blue radio button on the top right hand side entitled: Academic Consideration Request (ACR) to submit this request.
For Extenuating Circumstances, Policy 167: Academic Consideration allows for a once per semester ACR request without supporting documentation if the absence is less than 3 days in duration and is not for a final exam/final assessment. Absences more than 3 days in duration and those that involve a final exam/final assessment, require documentation. Students must notify their instructor once a request for academic consideration is submitted. See Senate Policy 167: Academic Consideration.
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