CPSC 330: Applied Machine Learning

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

• course administrative info
• other course documents
• past exams
• Piazza
• class + office hours calendar

Lecture schedule

Lecture Zoom link: https://ubc.zoom.us/j/68297919419

#	Date	Topic	Related readings and links	vs. CPSC 340
1	Sep 8	Course intro		n/a
2	Sep 10	Decision trees	Assumed preparation: Decision tree video until 26:30, and then continue from 36:35 onwards.	less math
3	Sep 15	The fundamental tradeoff of ML (and the Golden Rule)	Assumed preparation: Fundamentals of learning video (47 min) part of the KNN video up to 29:00 on cross-validation (8 min)
4	Sep 17	k-nearest neighbours, transforming numeric features (and the Golden Rule)
5	Sep 22	Encoding categorical variables (and the Golden Rule)
6	Sep 24	Hyperparameter optimization, pipelines (and the Golden Rule)
7	Sep 29	Our first end-to-end analysis	Meaningless comparisons lead to false optimism in medical machine learning, Damage Caused by Classification Accuracy and Other Discontinuous Improper Accuracy Scoring Rules	more depth
8	Oct 1	Logistic regression (binary and multi-class), CountVectorizer, predict_proba	no video	less depth on log reg, more on features
9	Oct 6	Evaluation metrics for classification	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
10	Oct 8	Linear regression, feature importances		more depth on feature importances, less on linear regression
11	Oct 13	Evaluation metrics for regression		more depth on error metrics
12	Oct 15	Ensembles		n/a
	Oct 20	MIDTERM
13	Oct 22	Feature engineering, feature selection	Feature selection article	feature engineering is new, less depth on feature selection
14	Oct 27	Natural language processing		new
15	Oct 29	Neural networks & computer vision	But what is a Neural Network?	less depth
16	Nov 3	Nearest neighbours for product similarity		less depth
17	Nov 5	Time series data	Humour: The Problem with Time & Timezones	new
18	Nov 10	Survival analysis	Calling Bullshit video 4.1, Medium article (contains some math)	new
19	Nov 12	Clustering		less depth
20	Nov 17	Outliers		different angle
21	Nov 19	Model deployment (or move to Dec 1)		new
22	Nov 24	Communicating your results	Communication in Data Science blog post; Calling BS videos Chapter 1 (5 video total)	new
23	Nov 26	Communicating your results, continued	Calling BS videos Chapter 6 (6 short videos, 47 min total)	new
24	Dec 1	Ethics	Calling BS videos Chapter 5 (6 short videos, 50 min total)	new
25	Dec 3	Leftovers; Conclusion

Homework schedule

#	Due Date	Topic	Associated lectures
1	Mon Sep 14	numpy/pandas	n/a
2	Mon Sep 21	TBD	2, 3, 4

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.