UBC CPSC 330: Applied Machine Learning (2024s)

Introduction

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the 2024S1 version, May-June 2024.

Instructor

Mehrdad Oveisi

• moveisi@cs.ubc.ca
• LinkedIn.com/in/oveisi (Feel free to connect on LinkedIn)
• Google Scholar
• Office hours:
  • When: Mon, Wed, Fri from 17:00 to 18:00 (as long as there are questions)
  • Where: MCLD 2018
  • Who: Students form both sections are welcome to attend all office hours.
  • For more details see class meetings on syllabus.

Class Schedule

Section	Location	Day	Lecture	Office Hour	OH Held By
911	DMP 310	Mon, Wed, Fri	10:00 - 13:00	13:00 - 14:00	TAs
912	MCLD 2018	Mon, Wed, Fri	14:00 - 17:00	17:00 - 18:00	TAs and Instructor

• For more details see class meetings on syllabus.

Course Coordinator

Liam Salt

• cpsc330-admin@cs.ubc.ca

Please email Liam Salt at the above email address for all administrative concerns such as CFA accommodations or exemptions due to sickness or extenuating circumstances.

Teaching Assistants

• Please see TAs on syllabus.

License

© 2023 Varada Kolhatkar and Mike Gelbart

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

• Syllabus / administrative info
• Calendar and due dates
• Course videos YouTube channel
• PrairieLearn (course assessments)
• Canvas (homework solutions, etc.)
• Piazza (course announcements and discussions)
• Setting up coding environment
• Other course documents
• iClicker Cloud

Compact course schedule (tentative)

IMPORTANT NOTE
As a general rule, summer terms are quite compact and thus time management is crucial to keep up with the course content and the deadlines. More precisely, based on the university calendar, the number of Teaching Days is 63 in winter terms and it is 28 in summer terms. That means there will be 2.25 (63÷28) times more content to learn per week, and 2.25 times faster pace for the homework due dates. In other words, you are expected to learn and deliver the same amount of work compared to winter terms, but do it 2.25 times faster! For this reason, time management is of utmost importance in order to succeed in the course.

The following chart is a very compact version of the course tentative schedule.

 

The following sections provide for more detailed course schedule.

Deliverable due dates (tentative)

Assessment	Due date	Where to find?	Where to submit?
Syllabus quiz	May 17, 23:00	PrairieLearn	PrairieLearn
hw1	May 18, 23:00	Github repo	PrairieLearn
hw2	May 21, 23:00	Github repo	PrairieLearn
hw3	May 25, 23:00	Github repo	PrairieLearn
hw4	May 30, 23:00	Github repo	PrairieLearn
Midterm	June 4, 18:00 Details on Piazza	PrairieTest	PrairieTest (in-person)
hw5	June 08, 23:00	Github repo	PrairieLearn
hw6	June 11, 23:00	Github repo	PrairieLearn
hw7	June 15, 23:00	Github repo	PrairieLearn
hw8	June 20, 23:00	Github repo	PrairieLearn
Final exam	[TBA] Final exam schedule	PrairieTest	PrairieTest (in-person)
Final exam viewing	[TBA] Perhaps before July 12	CBTF	In-person only (online not possible)

Lecture schedule (tentative)

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/updated as they become available.

Lectures	Date	Topic	Assigned videos	vs. CPSC 340
01	May 13	Course intro	📹 Pre-watch: None Recap video (after lecture): 1.0	n/a
		Part I: ML fundamentals and preprocessing
02	May 13 & 15	Decision trees	📹 Pre-watch: 2.1, 2.2 After lecture: 2.3, 2.4	less depth
03	May 15	ML fundamentals	📹 Pre-watch: 3.1, 3.2 After lecture: 3.3, 3.4	similar
04	May 17	$k$-NNs and SVM with RBF kernel	📹 Pre-watch: 4.1, 4.2 After lecture: 4.3, 4.4	less depth
05	May 17 & 22	Preprocessing, sklearn pipelines	📹 Pre-watch: 5.1, 5.2 After lecture: 5.3, 5.4	more depth
06	May 22	More preprocessing, sklearn ColumnTransformer, text features	📹 Pre-watch: 6.1, 6.2	more depth
07	May 24	Linear models	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
08	May 24 & 27	Hyperparameter optimization, overfitting the validation set	📹 Pre-watch: 8.1,8.2	different
09	May 27	Evaluation metrics for classification	📹 Pre-watch: 9.2,9.3,9.4	more depth
10	May 29	Regression metrics	📹 Pre-watch: 10.1	more depth on metrics less depth on regression
11	May 31	Ensembles	📹 Pre-watch: 11.1,11.2	similar
12	May 31 & Jun 3	Feature importances, model interpretation	📹 Pre-watch: 12.1,12.2	feature importances is new, feature engineering is new
13	Jun 3	Feature engineering and feature selection	None	less depth
	[TBA]	Midterm
		Part II: Unsupervised learning, transfer learning, different learning settings
14	Jun 5	Clustering	📹 Pre-watch: 14.1,14.2,14.3	less depth
15	Jun 5 & 7	More clustering	Post-lecture: 15.1, 15.2, 15.3	less depth
16	Jun 7	Simple recommender systems	None	less depth
17	Jun 10	Text data, embeddings, topic modeling	📹 Pre-watch: 16.1,16.2	new
18	Jun 10 & 12	Neural networks and computer vision		less depth
19	Jun 12	Time series data	(Optional) Humour: The Problem with Time & Timezones	new
20	Jun 14	Survival analysis	📹 (Optional but highly recommended) Calling Bullshit 4.1: Right Censoring	new
		Part III: Communication, ethics, deployment
21	Jun 17	Ethics	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
22	Jun 17 & 19	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
23	Jun 19	Model deployment and Conclusions		new
24		(optional reading) Stochastic Gradient Descent
25		(optional reading) Combining Multiple Tables