| 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): |
| ||||||||||||||
| Reference Text(s): |
| ||||||||||||||
| 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 | Listed on the course shell | ||||||||||||||
| 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, closed book (covers weeks 1-6). Final exam, closed book (covers weeks 6 - last module). | ||||||||||||||
| Other Evaluation Information | Laboratories There are 4 practical assignments in this course. | ||||||||||||||
| Teaching Methods | The course is delivered in person. All communication 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. | ||||||||||||||
Week | Hours | Chapters / | Topic, description |
|---|---|---|---|
1 | 3 | Introduction and General Concepts of Machine Learning and AI systems | |
2 | 3 | Introduction to Regression and Linear Regression | |
3 | 3 | Introduction to Classification and Performance Metrics | |
4 | 3 | Logistic Regression, and Softmax Regression | |
5 | 3 | Introduction to Unsupervised Learning and algorithms such as K-means, NN Clustering | |
6 | 3 | Introduction of Multiplayer Neural Networks - Forward Propagation | |
7 | 3 | Midterm | |
8 | 3 | Introduction of Deep Neural Networks and CNNs | |
9 | 3 | Introduction of Multiplayer Neural Networks - Backpropagation | |
10 | 3 | Advice on Applying Machine Learning Algorithms. Bias and Variance. | |
11 | 3 | Principal Component Analysis | |
12 | 3 | Bayesian Decision Theory | |
13,14 | 6 | Final Tutorials, Final Exam |
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: Classification |
5 | Lab 3 | Lab Assignment 3: Unsupervised Learning |
6 | Lab 4 | Lab Assignment 4: Multilayer Neural Network |
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 Library provides research workshops and individual assistance. If the University is open, there is a Research Help desk on the second floor of the library, or students can use the Library's virtual research help service to speak with a librarian.
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.
If taking a remote course, familiarize yourself with the tools you will need to use for remote learning. The Remote Learning Guide for students includes guides to completing quizzes or exams in D2L Brightspace, with or without Respondus LockDown Browser and Monitor, using D2L Brightspace, joining online meetings or lectures, and collaborating with the Google Suite.
Academic Accommodation Support (AAS) is the university's disability services office. AAS works directly with incoming and returning students looking for help with their academic accommodations. AAS works with any student who requires academic accommodation regardless of program or course load.
Academic Accommodations (for students with disabilities) and Academic Consideration (for students faced with extenuating circumstances that can include short-term health issues) are governed by two different university policies. Learn more about Academic Accommodations versus Academic Consideration and how to access each.
At Toronto Metropolitan University, we recognize that things can come up throughout the term that may interfere with a student’s ability to succeed in their coursework. These circumstances are outside of one’s control and can have a serious impact on physical and mental well-being. Seeking help can be a challenge, especially in those times of crisis.
If you are experiencing a mental health crisis, please call 911 and go to the nearest hospital emergency room. You can also access these outside resources at anytime:
If non-crisis support is needed, you can access these campus resources:
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.