CPSC 340/540 - Machine Learning and Data Mining (2026W1)

Lectures Sections (beginning Sep 9th):

1pm - 1:50pm (Monday/Wednesday/Friday in DMP 310)
2pm - 2:50pm (Monday/Wednesday/Friday in DMP 310)

Instructors: Prajeet Bajpai and Johnathan Wong

Instructor office hours: Office hours will be held immediately following the second lecture each Wednesday (i.e. from 3-4pm). You can walk with the instructor from lecture, or meet them at the office hour location directly.

Tutorials (beginning Sep 16):

Starting in the second week of classes, we will have weekly tutorials run by the TAs. These often spend time on the most commonly misunderstood things from lectures over the past week, backfilling knowledge from prerequisite courses that people may be rusty on yet is helpful to be fluent in, and doing things like going through provided assignment code, reviewing background material, reviewing big concepts, and/or doing exercises. You can register for particular tutorial sections if you want to save a seat at a particular time, but note that you do not need to register in a tutorial section and you can attend whichever one you like. Attending them is optional, but recommended.

T1A: Tue, 4-5pm
T1B: Thu, 4-5pm
T1C: Thu, 2-3pm
T1D: Mon, 5-6pm
T1E: Tues, 1-2pm
T1F: Thu, 12-1pm
T1G: Thu, 1-2pm
T1H: Wed, 4-5pm

Teaching assistants:

TA Office Hours:

Coming soon

Frequently Answered Questions

Midterm date

Synopsis: We introduce basic principles and techniques in the fields of data mining and machine learning. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We will focus on many of the core data mining and machine learning technologies, with motivating applications from a variety of disciplines.

Registration: Undergraduate and graduate students from any department are welcome to take the course. Undergraduate students should enroll in CPSC 340 while graduate students should enroll in CPSC 540 (when it is offered; CPSC 540 also has an extra small project component). Below are more details on registration for each course:

The majority of the seats in 340 are reserved for undergraduate computer science majors. For other students, to enroll in the course you need to sign up for the wait list. Note that all students on the wait list are typically accepted into the course. If 340 becomes full then signing up for the waiting list is the only way to enroll in the course. In previous years all students on the wait list were ultimately accepted into the course.

Prerequisites:

Basic algorithms and data structures (CPSC 221, or both of CPSC 260 and EECE 320 as well as one of CPSC 210, EECE 201, or EECE 309).
Linear algebra (one of MATH 152, 221, or 223).
Probability (one of STAT 241, STAT 251, ECON 325, ECON 327, MATH 302, STAT 302, or MATH 318).
Multivariate calculus (one of MATH 200, 217, 226, 253, or 263).

Students who do not meet these requirements should consider taking CPSC 330 ("Applied Machine Learning").

Textbook: There is no required textbook for the class. A introductory book that covers many (but not all) the topics we will discuss is the Artificial Intelligence book of Rusell and Norvig (AI:AMA) or the Artificial Intelligence book of Poole and Mackworth (you may need these for other classes). More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy's Machine Learning: A Probabilistic Perspective (ML:APP) which can be accessed through the library here, and Bishop's Pattern Recognition and Machine Learning (PRML). For books with a bigger focus on data mining, see Introduction to Data Mining (IDM) and Mining of Massive DataSets.

Related Courses: The most related course is CPSC 330: Applied Machine Learning. This course has fewer prerequisities and covers some of the same material, but focuses more on applications rather than understanding ML ideas in depth. A discussion on the difference between CPSC 340 and similar courses in statistics written by a former student (Geoff Roeder) is available here (this was written in 2016 so may be out of date).

Grading (tentative):

340: Assignments 30%, Midterm 20%, Final 50%.
540: Assignments 25%, Midterm 15%, Final 40%, Project 20%.

Assignments: There are a total of 6 written assignments for this course. Please follow the instructions linked here to submit your assignments.

List of topics

We will roughly cover the following topics:

Supervised learning with frequencies and distances.
Data clustering, outlier detection, and association rules.
Linear prediction, regularization, and kernels.
Latent-factor models and collaborative filtering.
Neural networks and deep learning.

Lectures, Assignments, Related Readings, and Links

Date	Slides	Related Readings and Links	Homework and Notes
Sep 9	Motivation and Syllabus	What is Machine Learning? Machine Learning Rise of the Machines Talking Machine Episode 1 Mathematics for Machine Learning	Assignment 1 (pdf) Assignment 1 (tex/code/data)
Sep 11	Exploratory Data Analysis	Gotta Catch'em all Why Not to Trust Statistics Visualization Types Google Chart Gallery Other Tools
Sep 14	Decision Trees	A Visual Introduction to Machine Learning, Decision Trees Entropy What is Big O Notation? AI:AMA 19.2-3, ESL: 9.2, ML:APP 16.2	Big-O Notes
Sep 16	Fundamentals of Learning	7 Steps of Machine Learning IID Cross-validation Bias-variance No Free Lunch AI:AMA 19.4-5, ESL 7.1-7.4, 7.10, ML:APP 1.4, 6.5	Course Notation Guide
Sep 18	Probabilistic Classifiers	Conditional probability (demo) Naive Bayes Probabilities and Battleship AI:AMA 12.6, ESL 4.3, ML:APP 2.2, 3.5, 4.1-4.2	Probability Notes Probability Slides
Sep 21	Non-Parametric Models	K-nearest neighbours Decision Theory for Darts Norms AI:AMA 19.7, ESL 13.3, ML:APP 1.4	Assignment 1 due Assignment 2 (pdf) Assignment 2 (tex/code/data)
Sep 23	Ensemble Methods	Ensemble Methods Random Forests Empirical Study Kinect AI:AMA 19.8, ESL: 7.11, 8.2, 15, 16.3, ML:APP 6.2.1, 16.2.5, 16.6
Sep 25	Clustering	Clustering K-means clustering (demo) K-Means++ (demo) IDM 8.1-8.2, ESL: 14.3
Sep 28	More Clustering	DBSCAN (video, demo) Hierarchical Clustering Phylogenetic Trees IDM 8.4
Sep 30	No Class: National Day for Truth and Reconciliation
Oct 2	Outlier Detection	Empirical Study IDM 8.3, ESL 14.3.12, ML:APP 25.5
Oct 5	Least Squares	Linear Regression (demo, 2D data, 2D video) Least Squares Essence of Calculus Partial Derivative Gradient ESL 3.1-2, ML:APP 7.1-3, AI:AMA 19.6	Assignment 2 due Assignment 3 (pdf) Assignment 3 (tex/code/data)
Oct 7	Nonlinear Regression	Why should one learn machine learning from scratch? Essence of Linear Algebra Matrix Differentiation Fluid Simulation (video) ESL 5.1, 6.3	Linear Algebra Notes Linear/Quadratic Gradients
Oct 9	Gradient Descent	Gradient Descent Convex Functions
Oct 12	No Class: Thanksgiving
Oct 14	Robust Regression	ML:APP 7.4
Oct 16	Feature Selection	Genome-Wide Association Studies AIC, BIC ESL 3.3 , 7.5-7
Oct 19	Regularization	ESL 3.4., ML:APP 7.5, AI:AMA 19.4	Assignment 3 due
Oct 21	More Regularization	RBF video RBF and Regularization video ESL 6.7, ML:APP 13.3-4
Oct 23	Linear Classifiers	Perceptron ESL 4.5, ML:APP 8.5	Assignment 4 (pdf) Assignment 4 (tex/code/data)
Oct 26	More Linear Classifiers	Support Vector Machines ESL 4.4, 12.1-2, ML:APP 8.1-3, 9.5 14.5, AI:AMA 19.6
Oct 28	Feature Engineering	Gmail Priority Inbox
Oct 30	Kernel Trick	ESL 12.3, ML:APP 14.1-4
Nov 2	Stochastic Gradient	Stochastic Gradient Descent, Theory and Practice ML:APP 8.5
Nov 4	Boosting, Start of MLE	AdaBoost (video) XGBoost (video) ML:APP 16.4
Nov 6	MLE and MAP	Maximum Likelihood Estimation ML:APP 9.3-4	Assignment 5 (pdf) Assignment 5 (tex/code/data)
Nov 9	No Class: Midterm Break
Nov 11	No Class: Midterm Break
Nov 13	PCA	Principal Component Analysis ESL 14.5, IDM B.1, ML:APP 12.2
Nov 16	More PCA	Making Sense of PCA SVD Eigenfaces	Max and Argmax Notes
Nov 18	Sparse Matrix Factorization	Non-Negative Matrix Factorization (original - access from UBC) ESL 14.6, ML: APP 13.8
Nov 20	Recommender Systems & MDS	Recommender Systems Netflix Prize	Assignment 6 (pdf) Assignment 6 (tex/code/data)
Nov 23	Neural Networks	Google Video What is a Neural Network? Interactive Guide ML:APP 16.5, ESL 11.1-4, AI:AMA 21.1
Nov 25	Deep Learning	Fortune Article Deep Learning References Alchemy ML:APP 28.3, ESL 11.5, AI:AMA 21.2 and 21.4-5
Nov 27	Deep Learning	But what is a convolution?
Nov 30	Convolutions	Convolutional Neural Networks ML:APP 28.4, ESL 11.7, AI:AMA 21.3
Dec 2	CNNs	Convolutional Neural Networks ML:APP 28.4, ESL 11.7, AI:AMA 21.3
Dec 4	More CNNs and Conclusion	Convolutional Neural Networks ML:APP 28.4, ESL 11.7, AI:AMA 21.3	Assignment 6 due
Dec 7	Guest Lecture
Final	TBD

Mike's Demos

In semesters where Mike Gelbart taught the course, he included Jupyter notebooks associated with most lectures. These notebooks are available here (note that the lecture numbers may not exactly match the current semester's course).

Future Homeworks and Lectures

It is possible to search for and potentially find future homeworks and lectures from previous runnings of the course, however, be aware that both of these can change from year to year and that you are responsible for this year's materials.

Related courses that have online notes

Introduction to Machine Learning (Alberta)
Practical Machine Learning (Berkeley)
Machine Learning (MIT)
Machine Learning (CMU)
Course in Machine Learning (Maryland)
Principals of Knowledge Discovery in Data (Alberta)
Data Mining (CMU)

Academic Integrity Policy

Academic integrity is a commitment to upholding the values of honesty, respect, integrity, and accountability in academic work. It is foundational to teaching and learning and is a shared value and commitment of all members of the UBC community. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not attempt to gain an unfair academic advantage or benefit, nor should you help others do the same.

Use of AI tools

Students are strongly encouraged to attempt all assignments on their own. For any assignments where AI tools were used by the student, the student must declare:

which AI tools, if any, were used
which parts of the assignment they were used for.

Regardless of whether or how AI tools were used, students are responsible for the accuracy, quality, and understanding of everything they submit, and should be prepared to explain the reasoning, code, or analysis represented in their work if asked.

The midterm and final exam for this course will be paper-based; no AI use is permitted for these assessments.

Academic misconduct

Violations of academic integrity (i.e., academic misconduct) may lead to serious consequences. For more details, please see UBC's Academic Integrity website and the Academic Calendar Statement on Academic Misconduct.