UBC CPSC 330: Applied Machine Learning (2025W1)

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the current version (Sep-Dec 2025).

The teaching team

Instructors

Section	Instructor	Contact	When	Where
101	Giulia Toti	gtoti@cs.ubc.ca	Tue & Thu, 15:30–16:50	DMP 310
102	Varada Kolhatkar	kvarada@cs.ubc.ca	Tue & Thu, 11:00–12:20	DMP 310
103	Giulia Toti	gtoti@cs.ubc.ca	Tue & Thu, 17:00–18:20	DMP 310

Course co-ordinator

• Anca Barbu (cpsc330-admin@cs.ubc.ca), please reach out to the course co-ordinator for: admin questions, extensions, academic concessions etc. Include a descriptive subject, your name and student number, this will help us keep track of emails.

TAs

License

© 2025 Varada Kolhatkar, Mike Gelbart, Giulia Toti

Software licensed under the MIT License, non-software content licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. See the license file for more information.

Important links

• Calendar

• Course GitHub page

• Course Jupyter book

• Canvas

• Piazza

• iClicker Cloud Section 001, iClicker Cloud Section 002, iClicker Cloud Section 003

• Gradescope

• Course videos YouTube channel

• Syllabus / administrative info

• Other course documents

Deliverable due dates (tentative)

Usually the homework assignments will be due on Mondays (except next week) and will be released on Tuesdays. We’ll also add the due dates in the Calendar. If you find inconsistencies in due dates, follow the due date in the Calendar. For this course, we’ll assume that the Calendar is always right!

Assessment	Due date	Where to find?	Where to submit?
hw1	Sept 09, 11:59 pm	GitHub repo	Gradescope
hw2	Sept 15, 11:59 pm	GitHub repo	Gradescope
Syllabus quiz	Sept 19, 11:59 pm	PrairieLearn	PrairieLearn
hw3	Sept 29, 11:59 pm	GitHub repo	Gradescope
hw4	Oct 06, 11:59 pm	GitHub repo	Gradescope
Midterm 1	Oct 15 and Oct 16	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)
hw5	Oct 27, 11:59 pm	GitHub repo	Gradescope
hw6	Nov 03, 11:59 pm	GitHub repo	Gradescope
Midterm 2	Nov 13 and Nov 14	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)
hw7	November 17, 11:59 pm	GitHub repo	Gradescope
hw8	November 24, 11:59 pm	GitHub repo	Gradescope
hw9	December 05, 11:59 pm	GitHub repo	Gradescope
Final exam	TBA	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)

Lecture schedule (tentative)

Live lectures: The lectures will be in-person. The location can be found in the Calendar.

This course will be run in a semi flipped classroom format. There will be pre-watch videos for many lectures, at least in the first half of the course. All the videos are available on YouTube and are posted in the schedule below. Try to watch the assigned videos before the corresponding lecture. During the lecture, we’ll summarize the important points from the videos and focus on demos, iClickers, and Q&A.

We’ll be developing lecture notes directly in this repository. So if you check them before the lecture, they might be in a draft form. Once they are finalized, they will be posted in the Course Jupyter book.

Date	Topic	Assigned videos	vs. CPSC 340
Sep 2	UBC Imagine Day - no class
Sep 4	Course intro	📹 Pre-watch: 1.0	n/a
Sep 9	Decision trees	📹 Pre-watch: 2.1, 2.2, 2.3, 2.4	less depth
Sep 11	ML fundamentals	📹 Pre-watch: 3.1, 3.2, 3.3, 3.4	similar
Sep 16	\(k\)-NNs and SVM with RBF kernel	📹 Pre-watch: 4.1, 4.2, 4.3, 4.4	less depth
Sep 18	Preprocessing, sklearn pipelines	📹 Pre-watch: 5.1, 5.2, 5.3, 5.4	more depth
Sep 23	More preprocessing, sklearn ColumnTransformer, text features	📹 Pre-watch: 6.1, 6.2	more depth
Sep 25	Linear models	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
Oct 01	National Day for Truth and Reconciliation - no class
Oct 02	Hyperparameter optimization, overfitting the validation set	📹 Pre-watch: 8.1, 8.2	different
Oct 07	Evaluation metrics for classification	📹 Reference: 9.2, 9.3,9.4	more depth
Oct 09	Regression metrics	📹 Pre-watch: 10.1	more depth on metrics less depth on regression
Oct 14 and 15	Midterm 1 - no class
Oct 16	Ensembles	📹 Pre-watch: 11.1, 11.2	similar
Oct 21	Feature importances, model interpretation	📹 Pre-watch: 12.1,12.2	feature importances is new, feature engineering is new
Oct 23	Feature engineering and feature selection	None	less depth
Oct 28	Clustering	📹 Pre-watch: 14.1, 14.2, 14.3	less depth
Oct 30	More clustering	📹 Pre-watch: 15.1, 15.2, 15.3	less depth
Nov 04	Simple recommender systems		less depth
Nov 06	Text data, embeddings, topic modeling	📹 Pre-watch: 16.1, 16.2	new
Nov 11	UBC Midterm break - no class
Nov 13 and 14	Midterm 2 - no_class
Nov 18	Neural networks and computer vision		less depth
Nov 20	Time series data	(Optional) Humour: The Problem with Time & Timezones	new
Nov 25	Survival analysis	📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring	new
Nov 27	Communication	📹 (Optional but highly recommended) Calling BS videos Chapter 6 (6 short videos, 47 min total) Can you read graphs? Because I can’t. by Sabrina (7 min)	new
Dec 02	Ethics	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
Dec 04	Model deployment and conclusion		new

Reference Material

Click to expand!

Books

• A Course in Machine Learning (CIML) by Hal Daumé III

• Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Mueller and Sarah Guido.

• An Introduction to Statistical Learning

• The Elements of Statistical Learning (ESL)

• Data Mining: Practical Machine Learning Tools and Techniques (PMLTT)

• Artificial intelligence: A Modern Approach by Russell, Stuart and Peter Norvig.

• Artificial Intelligence 2E: Foundations of Computational Agents (2023) by David Poole and Alan Mackworth (of UBC!).

Online courses

• Machine Learning Crash Course

• Machine Learning (Andrew Ng’s famous Coursera course)

• Foundations of Machine Learning online course from Bloomberg.

• Machine Learning Exercises In Python, Part 1 (translation of Andrew Ng’s course to Python, also relevant for DSCI 561, 572, 563)

Misc

• A Visual Introduction to Machine Learning (Part 1)

• A Few Useful Things to Know About Machine Learning (an article by Pedro Domingos)

• Metacademy (sort of like a concept map for machine learning, with suggested resources)

• Machine Learning 101 (slides by Jason Mayes, engineer at Google)

Syllabus

The syllabus is available here.

Enjoy your learning journey in CPSC 330: Applied Machine Learning!