Ali Ghodsi

Course at a glance

Overview

Course STAT 841 / CM 763

Term Fall 2026

Instructor Ali Ghodsi

Class meetings 4:00-5:20 p.m. (meeting days to be confirmed)

Location MC 4042

This page is the public home for the course topics, slide releases, and assignments. Enrollment-specific information, submissions, deadlines, and grades are maintained in Waterloo Learn.

Course support

Office Hours and Course Support

Instructor office hours

Ali Ghodsi

Wednesdays, 1–2 p.m.

M3 4009

Come to the instructor for: questions about course content or the final project.

Teaching assistant office hours

Assignment and coding support

Go to the TAs for: questions about assignments or coding.

Nafisat Ibrahim

Office hours: Fridays, 9–10 a.m.
Location: M3 2101

Jerry Zhen

Office hours: Thursdays, 1–2 p.m.
Location: M3 2101

From the course outline

Course details

Textbooks and references

There is no mandatory textbook. The following references cover substantial portions of the course:

The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Pattern Recognition and Machine Learning, Christopher M. Bishop.
Machine Learning: A Probabilistic Perspective, Kevin P. Murphy.

Assessment and evaluation

Component	Description	Weight
Assignments	Four individual assignments at 12.5% each	50%
Paper presentation	Small-group presentation of a selected research paper	10%
Group final project	Research or applied classification project	40%
Total	—	100%

Tentative assessment dates

Assessment	Release	Due
Assignment 1	Monday, September 28	Monday, October 19, 11:59 p.m.
Assignment 2	Monday, October 19	Monday, November 9, 11:59 p.m.
Assignment 3	Monday, November 9	Monday, November 23, 11:59 p.m.
Assignment 4	Monday, November 23	Monday, December 7, 11:59 p.m.
Paper presentation	Paper-selection process to be announced	Scheduled presentation date
Group final project	To be announced	Monday, December 21, 11:59 p.m.

All dates are tentative and may change. Changes will be announced.

Latest updates

Announcements

Course site

Welcome to STAT 841 / CM 763

This page will be updated throughout the term. Slides and assignments will be posted below as they are released.

Communication

Use Piazza for course discussion

Questions about lectures and course content should be posted on Piazza so that answers are available to the class.

Course schedule

Tentative lecture schedule

Lecture topics and ordering are tentative and may be adjusted during the term.

Lecture	Coverage	Materials
Lecture 1	Course introduction; classification problems; hypothesis class, loss, and search; Bayes classifier and Bayes error	Lecture 1 slides
Lecture 2	Gaussian classifiers; LDA and QDA; covariance assumptions and decision boundaries	Lecture 2 slides
Lecture 3	Principal component analysis (PCA); variance-maximizing projections; Lagrange multipliers; covariance eigendecomposition and SVD; encoding and reconstruction	Lecture 3 slides
Lecture 4	PCA versus supervised class separation; Fisher's linear discriminant analysis for two and multiple classes; within- and between-class scatter; generalized eigenvalue formulation	Lecture 4 slides
Lecture 5	Logistic regression; binomial class probabilities; maximum likelihood; score and Hessian; Newton-Raphson and iteratively reweighted least squares (IRLS)	Lecture 5 slides
Lecture 6	Neural-network history; perceptron model and learning; linear separability and convergence; feedforward and deep networks; backpropagation via the chain rule; gradient-descent weight updates and epochs	Lecture 6 slides
Lecture 7	Model selection and complexity control; validation, cross-validation, regularization, and early stopping	Coming soon
Lecture 8	Radial basis function networks; localized features, training, and least-squares estimation	Coming soon
Lecture 9	Complexity control for RBF networks; regularization and selection of centres and bandwidths	Coming soon
Lecture 10	Spectral clustering and nonlinear dimensionality reduction; Laplacian eigenmaps and maximum variance unfolding	Coming soon
Lecture 11	Support vector machines; margins, hard-margin classification, and constrained optimization	Coming soon
Lecture 12	Soft-margin SVMs; slack variables, kernels, and nonlinear decision boundaries	Coming soon
Lecture 13	Metric learning; learned distances, embeddings, and supervised PCA	Coming soon
Lecture 14	Naive Bayes and k-nearest neighbours; conditional independence and local classification	Coming soon
Lecture 15	Convolutional neural networks; convolution, parameter sharing, equivariance, and pooling	Coming soon
Lecture 16	Training deep convolutional networks; regularization, random features, residual networks, and modern architectures	Coming soon
Lecture 17	Decision trees; recursive partitioning, splitting criteria, overfitting, and pruning	Coming soon
Lecture 18	Boosting; weak learners, adaptive reweighting, and stage-wise additive models	Coming soon
Lecture 19	Bagging, random forests, variance reduction, and ensemble comparison	Coming soon
Lecture 20	Semi-supervised learning, selected emerging methods, and integration of the course's main ideas	Coming soon

Class examples

Examples, Code & Data

Datasets, code, and notebooks used for demonstrations and examples are collected here independently of the lecture schedule.

Classification · Handwritten digits

Handwritten Digits 2 and 3

A 64 × 400 MATLAB matrix named X containing 400 vectorized 8 × 8 handwritten-digit images. Each column is one image: columns 1–200 are examples of the digit 2, and columns 201–400 are examples of the digit 3.

Download dataset (.mat)

Plots images at specified 2-D coordinates. For example, after PCA or FDA, the handwritten digits can be superimposed on their projected points to reveal patterns in the two-dimensional space.

MATLAB plotting function (.m)

LDA and QDA · PCA and FDA · Logistic regression

Classification and Feature Extraction Demo

An interactive MATLAB demo reviewing LDA, QDA, PCA, FDA, and logistic regression using three synthetic classification datasets. It steps through feature extraction, decision boundaries, and model evaluation.

Download MATLAB demo (.m)

Coursework

Assignments

Due Monday, October 19, 2026, at 11:59 p.m. Toronto time

STAT 841 / CM 763 — Assignment 1: Statistical Learning — Classification

Individual assignment · 100 marks

Assignment PDF Data ZIP

View full instructions

Download the assignment PDF and the data ZIP linked above. Extract the data ZIP before starting Question 6 and keep the four CSV filenames unchanged. Read the assignment instructions carefully; the Deliverables box at the end of each question specifies exactly what to submit.

Submission instructions

Crowdmark: Upload your written solutions, tables, and figures to the corresponding assignment questions. Keep full code in your Learn submission.
Learn: Submit one ZIP file named WatIAM_A1.zip to the Assignment 1 Dropbox in Learn, replacing WatIAM with your Waterloo username. Include Q3.py, Q5.py, and Q6.py (or the corresponding .R or .m files), any required helper files, Q6_cv_results.csv, Q6_predictions.csv, and a short README giving run commands, software/dependencies, and random seeds.

Use Python, R, or MATLAB and follow the programming requirements in the assignment PDF.

Required Generative AI Use Survey

Generative AI use is permitted. After completing the assignment, open Learn and select Submit → Surveys → Assignment 1: Generative AI Use Survey. Everyone must complete the survey, including students who used no generative AI; in that case, answer No to all five questions. The survey is not anonymous so completion can be checked. Your survey responses do not affect your assignment grade. Open Learn.

Individual Verification of Understanding

You may be asked to complete a brief individual quiz or participate in an oral discussion about your assignment. This may include explaining your reasoning and solutions, answering related questions, or explaining and making small modifications to your submitted code. You must demonstrate your own understanding of all submitted work, including work developed with permitted generative AI assistance. These checks must be completed without generative AI or assistance from others. If you cannot demonstrate adequate understanding of your submitted work, your overall assignment mark may be reduced.

Late Submission Policy

Submissions received up to 24 hours after the deadline receive a 10% penalty. Submissions more than 24 hours late receive a grade of zero.

Course tools

Piazza & Waterloo Learn

Piazza

Use Piazza for announcements, lecture questions, clarifications, and class discussion.

Go to Piazza

Waterloo Learn (D2L)

Use Learn for graded work, submissions, private course documents, deadlines, and grades.

Open Waterloo Learn

Sign-in may be required. Students should use the course-specific links available in their enrolled Piazza and Learn accounts.

How the course runs

Course information

Course materials

Public slides are posted with the tentative topics. Additional or restricted materials are shared through Learn.

Assessment

Public assignment instructions are posted on this page. Submission links, feedback, and official grades are provided through Learn.

Questions

For assignment and coding questions, attend TA office hours. For course-content or final-project questions, attend instructor office hours. Use a private Learn message or email for personal matters.

Deadlines

Consult Learn and Piazza for authoritative dates, room information, schedule changes, and deadlines.