nashid/cpsc330

CPSC 330: Applied Machine Learning

UBC CPSC 330: Applied Machine Learning (2020W1)

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 2020). An earlier version from Jan-Apr 2020 can be found here.

Instructor: Mike Gelbart

Important links

• syllabus / administrative info
• other course documents
• past exams
• Piazza
• class + office hours calendar
• Zoom links on Canvas

Lecture schedule

The lectures will be on Zoom. They can be joined through Canvas here. If you would like to join the lectures but cannot login to Canvas (presumably because you're not enrolled in the course) please email Mike and I will give you the link.

#	Date	Topic	Related readings and links	vs. CPSC 340
	Sep 8	UBC Imagine Day - no class
1	Sep 10	Course intro		n/a
			Dataset of the week: predicting whether CPSC 330 students like cilantro
2	Sep 15	Decision trees		less depth
3	Sep 17	The fundamental tradeoff of ML (and the Golden Rule)		similar
			Dataset of the week: sentiment analysis of movie reviews
4	Sep 22	Logistic regression, word counts, predict_proba (and the Golden Rule)	Meaningless comparisons lead to false optimism in medical machine learning	less depth
5	Sep 24	Hyperparameter optimization, pipelines (and the Golden Rule)		more depth
			Dataset of the week: Predicting income from census data
6	Sep 29	Encoding categorical variables (and the Golden Rule)		more depth
7	Oct 1	missing data, transforming numeric features		more depth
			Dataset of the week: detecting credit card fraud
8	Oct 6	Evaluation metrics for classification	Damage Caused by Classification Accuracy and Other Discontinuous Improper Accuracy Scoring Rules, Optional watching: video: precision and recall (until 8:29), video: ensembles (until 37:48), then continuing the same video until 46:33 for random forests; Classification vs. Prediction	more depth
9	Oct 8	Ensembles		similar
			Dataset of the week: predicting housing prices
10	Oct 13	Linear regression, feature importances		more depth on feature importances, less on linear regression
11	Oct 15	Evaluation metrics for regression		more depth
12	Oct 20	Feature engineering, feature selection	Feature selection article	more on feature engineering, less on feature selection
	Oct 22	MIDTERM	study materials
13	Oct 27	Natural language processing		new
14	Oct 29	Neural networks & computer vision	But what is a Neural Network?	less depth
15	Nov 3	Nearest neighbours for product similarity		less depth
16	Nov 5	Time series data	Humour: The Problem with Time & Timezones	new
17	Nov 10	Survival analysis	Calling Bullshit video 4.1, Medium article (contains some math)	new
18	Nov 12	Clustering		less depth
19	Nov 17	Outliers		different angle
20	Nov 19	Model deployment (or move to Dec 1)		new
21	Nov 24	Communicating your results	Communication in Data Science blog post; Calling BS videos Chapter 1 (5 video total)	new
22	Nov 26	Communicating your results, continued	Calling BS videos Chapter 6 (6 short videos, 47 min total)	new
23	Dec 1	Ethics	Calling BS videos Chapter 5 (6 short videos, 50 min total)	new
24	Dec 3	Leftovers; Conclusion

Homework schedule

#	Due Date	Associated lectures
1	Tue Sep 15 11:59pm	prerequisites
2	Mon Sep 21 11:59pm	2, 3

Attribution

Thank you to Tomas Beuzen and Varada Kolhatkar for significant contributions to the course materials.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.