UBC CPSC 330: Applied Machine Learning (2023W2)

Introduction

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the version for 2023W2 (Jan-Apr 2024). Some previous offerings:

• 2020w1 by Mike Gelbart
• 2023s by Mehrdad Oveisi
• 2023w1 by Varada Kolhatkar and Andrew Roth
• 2022w2 by Giulia Toti, Mathias Lecuyer, and Amir Abdi

Instructors:

Mathias Lecuyer (201), Mehrdad Oveisi (202).

Class Schedule

Section	Days	Lecture	Location
201	Tue, Thu	09:30 - 10:50	LSK 200
202	Tue, Thu	15:30 - 16:50	MCML 360

Course Coordinator

Mehrdad Oveisi:

• [email protected]
• Please email Mehrdad Oveisi at the above email address for all administrative concerns such as CFA accommodations, extensions or exemptions due to sickness or extenuating circumstances.

Important links

• Syllabus
• Course GitHub page
• Course videos YouTube channel
• Canvas
• Piazza: this is where all announcements will be made.
• Setting up the coding environment
• Other course documents

Deliverable due dates (tentative)

Assessment	Due date	Where to find?	Where to submit?
Syllabus quiz	Jan 15, 11:59pm	PrairieLearn (PL)	PL
hw1	Jan 15, 11:59pm	Github repo	PL
hw2	Jan 22, 11:59pm	Github repo	PL
hw3	Feb 5, 11:59pm	Github repo	PL
hw4	Feb 12, 11:59pm	Github repo	PL
hw5	Feb 26, 11:59pm	Github repo	PL
Midterm	Fri. March 1	ESB 1013 or HEBB 100 (see piazza)	In person
hw6	Mar 25, 11:59pm	Github repo	PL
hw7	Apr 2, 11:59pm	Github repo	PL
hw8	April 10, 11:59pm	Github repo	PL
Final exam	TBD	TBD	TBD

Lecture schedule (tentative)

Lectures:

• Watch the "Pre-watch" videos before each lecture.
• You will find lecture notes from each instructor in this repository. Lectures will be posted as they become available.

Lectures:

• The lectures will be in-person (see Class Schedule above for more details).
• All lecture files are subject to change without notice up until they are covered in class.
• You are expected to watch the "Pre-watch" videos before each lecture.
• You are expected to attend the lectures.
• You will find the lecture notes under the lectures in this repository. Lectures will be posted as they become available.

Date	Topic	Videos	vs. CPSC 340
Jan 9	Course intro	📹 Pre-watch: None Recap video (after lecture): 1.0	n/a
	Part I: ML fundamentals and preprocessing
Jan 11	Decision trees	📹 Pre-watch: 2.1, 2.2 After lecture: 2.3, 2.4	less depth
Jan 16	ML fundamentals	📹 Pre-watch: 3.1, 3.2 After lecture: 3.3, 3.4	similar
Jan 18	Snow day: no lecture
Jan 23	$k$-NNs and SVM with RBF kernel	📹 Pre-watch: 4.1, 4.2 After lecture: 4.3, 4.4	less depth
Jan 25	Preprocessing, sklearn pipelines	📹 Pre-watch: 5.1, 5.2 After lecture: 5.3, 5.4	more depth
Jan 30	More preprocessing, sklearn ColumnTransformer, text features	📹 Pre-watch: 6.1, 6.2	more depth
Feb 1	Linear models	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
Feb 6	Hyperparameter optimization, overfitting the validation set	📹 Pre-watch: 8.1,8.2	different
Feb 8	Evaluation metrics for classification	📹 Pre-watch: 9.2,9.3,9.4	more depth
Feb 13	Regression metrics	📹 Pre-watch: 10.1	more depth on metrics less depth on regression
Feb 15	Ensembles	📹 Pre-watch: 11.1,11.2	similar
Feb 19-23	Midterm Break (no classes)
Feb 27	Midterm review
Feb 29	No lecture (midterm catch-up)
Mar 1	Midterm	16:20 on Friday. ESB 1013 or HEBB 100. More details on Piazza
Mar 5	Feature importances, model interpretation	📹 Pre-watch: 12.1,12.2	feature importances is new, feature engineering is new
Mar 7	Feature engineering and feature selection	None	less depth
	Part II: Unsupervised learning, transfer learning, different learning settings
Mar 12	Clustering	📹 Pre-watch: 14.1,14.2,14.3	less depth
Mar 14	More clustering	Post-lecture: 15.1, 15.2, 15.3, 201 lecture recording	less depth
Mar 19	Simple recommender systems	None	less depth
Mar 21	Text data, embeddings, topic modeling	📹 Pre-watch: 16.1,16.2	new
Mar 26	Neural networks and computer vision		less depth
Mar 28	Time series data	(Optional) Humour: The Problem with Time & Timezones	new
Apr 2	Survival analysis	📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring	new
	Part III: Communication, ethics, deployment
Apr 4	Ethics	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
Apr 9	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
Apr 11	Model deployment		new