CPSC 340/540 - Machine Learning and Data Mining (2025W2)

Lectures Sections (beginning Jan 5th):

12pm - 1pm (Monday/Wednesday/Friday in DMP 310)
2pm - 3pm (Monday/Wednesday/Friday in DMP 310)

Instructors: Frank Wood and Mi Jung Park

Instructor office hours: When Dr. Park teaches she will have office hours at 2:50 (i.e. right after the second class ends, including walking back with anyone who wants to chat while walking, and then office hours will conclude in that professor's office). The hour will be 2:50-3:50. Students can go straight to the office and wait for the professor to return. Professor Park's office is ICICS X539. This schedule tells you which instructor is teaching which day (⚠ subject to change). It also has other important dates.

Tutorials (beginning Jan 12):

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.

T2B: Mon, 3-4pm - Shenran Wang
T2F: Mon, 4-5pm - Frederick Shpilevskiy
T2G: Tues, 12-1pm - Pierre Lardet
T2H: Tues, 1-2pm - George Cao
T2C: Tues, 2-3pm - Andrew Ahn
T2E: Wed, 4-5pm - George Cao
T2D: Wed, 5-6pm - Nima Norouzi
T2A: Fri, 4-5pm - Minuk Ma

Teaching assistants:

TA Office Hours:

Minuk Ma: Monday, 1-2pm (in-person). ICCS x150 - DLC Table 1
Nima Norouzi: Monday, 6-7pm (online). Zoom link
Shenren Wang: Tuesday, 2-3pm (in-person and online). ICCS X139, Zoom link
Pierre Lardet: Tuesday, 4-5pm (in-person). ICCS x150 - DLC Table 5
Fengzhe Shi: Wednesday, 1-2pm (online). Zoom link
Fengzhe Shi: Wednesday, 3-4pm (in-person). ICCS x150 - DLC Table 1
Mohammad Moshtaghifar: Thursday, 11am-12pm (in-person and online). ICCS x337, Zoom link
Frederick Schpilevskiy: Thursday, 1pm-2pm (in-person). ICCS X337.
Andrew Ahn - Friday, 11am-12pm (in-person). ICCS x150 - DLC Table 5
Bowen Cui - Friday, 1-2pm (in-person). ICCS X139.
Ali Mehrabian - Friday, 3-4pm (online). Zoom link

Frequently Answered Questions

Midterm information

Room: IRC 2
Date(s): Tuesday, February 24, 2026
Time: 5:00 - 7:00 PM
Midterm Conflict Survey

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
Jan 5	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)
Jan 7	Exploratory Data Analysis	Gotta Catch'em all Why Not to Trust Statistics Visualization Types Google Chart Gallery Other Tools
Jan 9	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
Jan 12	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
Jan 14	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
Jan 16	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 Withdrawal deadline Assignment 2 (pdf) Assignment 2 (tex/code/data)
Jan 19	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
Jan 21	Clustering	Clustering K-means clustering (demo) K-Means++ (demo) IDM 8.1-8.2, ESL: 14.3
Jan 23	More Clustering	DBSCAN (video, demo) Hierarchical Clustering Phylogenetic Trees IDM 8.4
Jan 26	Outlier Detection	Empirical Study IDM 8.3, ESL 14.3.12, ML:APP 25.5
Jan 28	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
Jan 30	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 Assignment 2 due Assignment 3 (pdf) Assignment 3 (tex/code/data)
Feb 2	Gradient Descent	Gradient Descent Convex Functions
Feb 4	Robust Regression	ML:APP 7.4
Feb 6	Feature Selection	Genome-Wide Association Studies AIC, BIC ESL 3.3 , 7.5-7
Feb 9	Regularization	ESL 3.4., ML:APP 7.5, AI:AMA 19.4
Feb 11	More Regularization	RBF video RBF and Regularization video ESL 6.7, ML:APP 13.3-4
Feb 13	Linear Classifiers	Perceptron ESL 4.5, ML:APP 8.5	Assignment 3 due
Feb 16	No Class: Midterm Break
Feb 18	No Class: Midterm Break
Feb 20	No Class: Midterm Break
Feb 23	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
Feb 25	Feature Engineering	Gmail Priority Inbox
Feb 27	Kernel Trick	ESL 12.3, ML:APP 14.1-4	Assignment 4 (pdf) Assignment 4 (tex/code/data)
Mar 2	Stochastic Gradient	Stochastic Gradient Descent, Theory and Practice ML:APP 8.5
Mar 4	Boosting, Start of MLE	AdaBoost (video) XGBoost (video) ML:APP 16.4
Mar 6	MLE and MAP	Maximum Likelihood Estimation ML:APP 9.3-4
Mar 9	PCA	Principal Component Analysis ESL 14.5, IDM B.1, ML:APP 12.2
Mar 11	More PCA	Making Sense of PCA SVD Eigenfaces	Max and Argmax Notes
Mar 13	Sparse Matrix Factorization	Non-Negative Matrix Factorization (original - access from UBC) ESL 14.6, ML: APP 13.8	Assignment 4 due Assignment 5 (pdf) Assignment 5 (tex/code/data)
Mar 16	Recommender Systems & MDS	Recommender Systems Netflix Prize
Mar 18	Neural Networks	Google Video What is a Neural Network? Interactive Guide ML:APP 16.5, ESL 11.1-4, AI:AMA 21.1
Mar 20	Guest Lecture
Mar 23	Deep Learning	Fortune Article Deep Learning References Alchemy ML:APP 28.3, ESL 11.5, AI:AMA 21.2 and 21.4-5
Mar 25	Deep Learning	But what is a convolution?
Mar 27	Convolutions	Convolutional Neural Networks ML:APP 28.4, ESL 11.7, AI:AMA 21.3	Assignment 5 due Assignment 6 (pdf) Assignment 6 (tex/code/data)
Mar 30	Guest Lecture
Apr 1	CNNs and RNNs
Apr 3	No Class: Easter Friday
Apr 6	No Class: Easter Monday
Apr 8	LSTMs, Attention, and Transformers
Apr 10	Generative Sampling + Conclusion		Assignment 6 due
Apr 25	Final

Frank's Recorded Lectures

During the COVID-19 pandemic, Frank recorded video lectures for the course. These videos are available here. Note that these videos may not exactly match the current semester's course, however in most cases the content will be very similar if not identical.

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)