christopher-eunho/cpsc330-2022w2

UBC CPSC 330: Applied Machine Learning (2022W2)

Introduction

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the current version (Jan-Apr 2023). Earlier versions can be found at these links:

• from Sep-Dec 2020 taught by Mike Gelbart
• from Jan-April 2022 taught by Giulia Toti
• from May-June 2022 taught by Mehrdad Oveisi
• from Sep-Dec 2022 taught by Varada Kolhatkar

Instructors: Giulia Toti (201), Mathias Lecuyer (202), Amir Abdi (203)

Important links

• Course GitHub page
• Course Jupyter book
• Course videos YouTube channel
• Canvas link
• Syllabus / administrative info
• Piazza (this is where all announcements will be made). Click here to enroll.
• Other course documents
• Past exams

Deliverable due dates (tentative)

Usually the homework assignments will be due on Mondays and will be released on Tuesdays.

Assessment	Due date	Where to find?	Where to submit?	Weight (%)
Syllabus quiz	Jan 16, 11:59pm	Canvas	Canvas	1%
hw1	Jan 16, 11:59pm	Github repo	Gradescope	3%
hw2	Jan 23, 11:59pm	Github repo	Gradescope	3%
hw3	Feb 1, 11:59pm	Github repo	Gradescope	4%
hw4	Feb 10, 11:59pm	Github repo	Gradescope	4%
Midterm	Feb 15 Wednesday	TBD	TBD	19 %
hw5	Feb 27, 11:59pm	Github repo	Gradescope	5%
hw6	Mar 13, 11:59pm	Github repo	Gradescope	4%
hw7	Mar 20, 11:59pm	Github repo	Gradescope	4%
hw8	TBD, 11:59pm	Github repo	Gradescope	3%
Final exam	TBD	TBD	TBD	50%

Lecture schedule (tentative)

Lectures will be on Tuesday and Thursday. Exact time and location change according to your section:

Section	Day	Time	Location
201	Tue/Thu	2:00 - 3:30	Geography 100
202	Tue/Thu	3:30 - 5:00	P. A. Woodward Instructional Resources Centre 3
203	Tue/Thu	5:00 - 6:30	Hugh Dempster Pavilion 310

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.

Date	Topic	Assigned videos and datasets	vs. CPSC 340
Jan 10	Course intro	📹 Pre-watch: None Recap video (after lecture): 1.0	n/a
	Part I: ML fundamentals and preprocessing
		Week 1 datasets: grade prediction toy dataset Canada USA cities toy dataset Housing Prices
Jan 12	Decision trees	📹 Pre-watch: 2.1, 2.2 After lecture: 2.3, 2.4	less depth
Jan 17	ML fundamentals	📹 Pre-watch: 3.1, 3.2 After lecture: 3.3, 3.4	similar
		Week 2 datasets: California housing Spotify Song Attributes
Jan 19	$k$-NNs and SVM with RBF kernel	📹 Pre-watch: 4.1, 4.2 After lecture: 4.3, 4.4	less depth
Jan 24	Preprocessing, sklearn pipelines	📹 Pre-watch: 5.1, 5.2 After lecture: 5.3, 5.4	more depth
		Week 3 dataset: California housing
Jan 26	More preprocessing, sklearn ColumnTransformer, text features	📹 Pre-watch: 6.1, 6.2	more depth
		Week 4 datasets: IMDB movie review
Jan 31	Linear models	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
		Week 5 datasets: Spotify Song Attributes Credit Card Fraud Detection
Feb 2	Hyperparameter optimization, overfitting the validation set	📹 Pre-watch: 8.1,8.2	different
Feb 7	Evaluation metrics for classification	📹 Pre-watch: 9.2,9.3,9.4	more depth
		Week 6 datasets: Kaggle House Prices data set Adult Census Income
Feb 9	Regression metrics	📹 Pre-watch: 10.1	more depth on metrics less depth on regression
Feb 14	Midterm review
Feb 15	Midterm	On Wednesday! Note the different time! More details will be posted on Piazza
Feb 16	No lecture
Feb 19-25	Reading week (no classes)
		Week 7 datasets: Adult Census Income Credit Card Dataset for Clustering
Feb 28	Ensembles	📹 Pre-watch: 11.1,11.2	similar
Mar 2	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 9	Clustering	📹 Pre-watch: 14.1,14.2,14.3	less depth
Mar 14	More clustering	None	less depth
		Week 9 datasets: Jester 1.7M jokes ratings dataset
Mar 16	Simple recommender systems	Post-lecture: 15.1, 15.2, 15.3	less depth
Mar 21	Text data, embeddings, topic modeling	📹 Pre-watch: 16.1,16.2	new
Mar 23	Neural networks and computer vision		less depth
Mar 28	Time series data	(Optional) Humour: The Problem with Time & Timezones	new
Mar 30	Survival analysis	📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring	new
	Part III: Communication, ethics, deployment
April 4	Ethics	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
Apr 6	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
Apr 13	Conclusions - TBD		new