SYDE 675 - Pattern Recognition - Winter 2024

Logistics

• Instructor: Ali Ayub

• Email: a9ayub@uwaterloo.ca

• Course Website: https://sites.google.com/view/aliayub/teaching/syde-675-winter-2024

• Time and Location: 

  • ThF 3:30 - 4:50 PM (E7, 4437)

• Office Hours: Thursday after class 4:50 PM - 05:50 PM or by email appointment (E7, 5402)

• TAs

  • Yuxian Huang (y675huan@uwaterloo.ca) 

  • Office Hours: Every other week, Wed 3:00 - 4:00 PM (Online)

• Course Syllabus: here

Grading

• Assignments: 3 x 18 = 54%.

• Project: 46%

Assignments

• There will be three assignments, posted every other week (All dates are tentative and can vary by a few days).

• Submit a zipped folder of your solution (pdf report, code, etc.) through LEARN.

• All assignments are due at 11:59 PM EST.

• No late submissions are accepted unless you have a legitimate reason. 

  1. Assignment 1: Posted on Jan 23, 2024, files, due Feb 10, 2024. 

  2. Assignment 2: Posted on Feb 13, 2024, files, due Feb 27, 2024. 

  3. Assignment 3: Posted on March 06 2024, files, due March 22, 2024. 

Project

NOTE: You need to get at least 50% in the project to pass the course.

You need to conduct a research project, which could be an attempt to beat the state-of-the-art performance on an interesting dataset, an unexpected application of pattern recognition algorithms to a different field, a novel algorithm to address a need in pattern recognition, or theoretical analyses of the performance of pattern recognition or machine learning algorithm (new or old). You could choose your project into the following categories:

• Literature survey: include the problem definition and cite at least 10 papers that you plan to survey.

• Empirical evaluation: include the problem definition that you want to study, and cite at least 5-8 papers that you plan to review.

• Algorithm design: include the problem definition and a justification for why the current approaches are not satisfactory for the given problem. Cite at least 5 papers related to the chosen problem.

Your project should

• relate to pattern recognition or machine learning (obviously)

• allow you to learn something new and interesting

• be significant and (ideally) publishable in a top pattern recognition, machine learning, or computer vision conference

There will be a project proposal due by March 1st (10% of the project grade). You are strongly suggested to remain in contact with the TA or instructor throughout the term to make sufficient progress on the project. A complete project report (and supporting materials, such as videos, supplementary files, code, etc.) is due by April 5th (11:59 PM). We expect the project report to be a maximum of 8 pages, excluding references. Use the NeurIPS or ICLR Latex template for your project report. In the final week of classes, we will have presentations for the project. Your presentations will contribute 10% towards your final project grade.

Your project report will be evaluated by its clarity, significance, rigor, presentation, and completeness.

Textbooks

There are no textbooks for the course. The following, however, are some excellent resources:

• An Introduction to Patten Recognition and Machine Learning, by Paul Fieguth

• Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020. freely available online

• Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. 2022. freely available online

• Neural Networks and Deep Learning, Nielsen, 2017 (link)

• Deep Learning, Goodfellow, Bengio, and Courville, 2016 (link)

Resources

• Colab

• Introduction to Numerical Computing with NumPy, by Alex Chabot-Leclerc.

• Here are some top conferences and journals in machine learning. They may be good places to look for project ideas.

  • NeurIPS

  • ICML

  • ICLR

  • COLT

  • AISTATS

  • JMLR

  • CVPR, ICCV, WACV

Policies

Academic Integrity: To maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect, and responsibility. Check the university website for more information.

Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt please be certain to contact the department’s administrative assistant who will provide further assistance.

Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offenses and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.

Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).

Note for Students with Disabilities: The Office for Persons with Disabilities (OPD), located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the OPD at the beginning of each academic term.

Mental Health: If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support.

On-campus Resources

• Campus Wellness https:uwaterloo.cacampus-wellness

• Counselling Services: counselling.services@uwaterloo.ca 519-888-4567 ext 32655 Needles Hall North 2nd floor, (NH 2401)

• MATES: one-to-one peer support program offered by the Federation of Students (FEDS) and Counselling Services: mates@uwaterloo.ca

• Health Services service: located across the creek from Student Life Centre, 519-888-4096.

Off-campus Resources

• Good2Talk (24/7): Free confidential helpline for post-secondary students. Phone: 1-866-925-5454

• Here 24/7: Mental Health and Crisis Service Team. Phone: 1-844-437-3247

• OK2BME: a set of support services for lesbian, gay, bisexual, transgender, or questioning teens in Waterloo. Phone: 519-884-0000 extension 213

Diversity: We intend that students from all diverse backgrounds and perspectives be well served by this course and that students’ learning needs be addressed both in and out of class. We recognize the immense value of the diversity in identities, perspectives, and contributions that students bring, and the benefit it has on our educational environment. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In particular:

• We will gladly honor your request to address you by an alternate/preferred name or gender pronoun. Please advise us of this preference early in the semester so we may make appropriate changes to our records.

• We will honor your religious holidays and celebrations. Please inform of us these at the start of the course.

• We will follow AccessAbility Services guidelines and protocols on how to best support students with different learning needs.