| Instructor(s) | Dr. Mohammed Saif [Coordinator] Office: EPH417 Phone: TBA Email: mohammed.saif@torontomu.ca Office Hours: Thursday 4:00 pm- 5:00 pm | ||||||||||||||
| 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 | 1- Sarah Kamoun, email: skamoun@torontomu.ca 2- Osama Harmouche, email: oharmouche@torontomu.ca 3- Syed Ammad Ali Shah, email: s10shah@torontomu.ca 4- Erfan Shahab, email: erfan.shahab@torontomu.ca | ||||||||||||||
| 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 (covers weeks 1-6). Final exam, during exam period, three hours, closed-book (covers all course materials). | ||||||||||||||
| Other Evaluation Information | Laboratories There are 4 practical assignments in this course. These are to be done individually 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 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. In addition, you will need to demonstrate your lab works to the TA. The assignments will consist of theoretical and practical parts and will require use of Python. | ||||||||||||||
| Teaching Methods | The course is delivered in person. All communications are online. All course materials are provided on the course shell. | ||||||||||||||
| Other Information | The Course Outline is tentative. Please refer to the course shell for the most up-to-date details. Students may use Generative AI (e.g. ChatGPT, Grammarly, Perplexity, DeepL Translator) for ideation and brainstorming but not for research or for writing anything (e.g., lab reports) that will be submitted for credit. Failure to stay within these limits will be considered a breach of Policy 60. | ||||||||||||||
Week | Hours | Chapters / | Topic, description |
|---|---|---|---|
1 | 3 | Introduction and General Concepts of Machine Learning and AI systems | |
2 | 3 | Linear Algebra Review. Optimization and Gradient Descent. Review of Probability Concepts | |
3 | 3 | Bayesian Decision Theory. Bayes Theorem and Decision Rule. Minimum Risk Action. Discriminant | |
4 | 3 | Bayesian Decision Theory (continued). | |
5 | 3 | Linear Discriminant Functions | |
6 | 3 | Linear Discriminant Functions/Algorithms | |
8 | 3 | Midterm | |
9 | 3 | Logistic Regression and Softmax Regression | |
10 | 3 | Introduction of Multilayer Neural Networks and Deep Learning | |
11 | 3 | Neural Networks Continued. Advice on using Neural | |
12 | 3 | Introduction to Unsupervised Learning and algorithms such as K-means, NN Clustering | |
13 | 3 | Principal Component Analysis | |
14 | 3 | Final Review/Final Exams |
Week | L/T/A | Description |
|---|---|---|
1-2 | Lab 0 | Lab Assignment 0: Intro to Python for Machine Learning |
3, 4, 5 | Lab 1 | Lab Assignment 1: Bayesian Decision Theory |
6, 8 | Lab 2 | Lab Assignment 2: Linear Discriminant Function |
9-10 | Lab 3 | Lab Assignment 3: Multilayer Neural Network |
11-12 | 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.
The University Libraries provide research workshops and individual consultation appointments. There is a drop-in Research Help desk on the second floor of the library, and students can use the Library's virtual research help service to speak with a librarian, or book an appointment to meet in person or online.
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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, always require documentation. Students must notify their faculty/contract lecturer once a request for academic consideration is submitted. See Senate Policy 167: Academic Consideration.
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We encourage all Toronto Metropolitan University community members to access available resources to ensure support is reachable. You can find more resources available through the Toronto Metropolitan University Mental Health and Wellbeing website.