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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

Lecture schedule

Live lectures: 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.

Lecture recordings: The lecture recordings can be accessed through the same Zoom page on Canvas here. From this page, navigate to the "Cloud Recordings" tab and you should see them there. The same lecture recordings will be posted here embedded in the schedule below.

Update: the videos are now available as a YouTube playlist here.

# Date Topic Recording Related readings and links vs. CPSC 340
Sep 8 UBC Imagine Day - no class
1 Sep 10 Course intro recording n/a
Dataset of the week: which CPSC 330 students like cilantro?
2 Sep 15 Decision trees recording less depth
3 Sep 17 The fundamental tradeoff of ML recording About Train, Validation and Test sets similar
Dataset of the week: sentiment analysis of movie reviews
4 Sep 22 Logistic regression, word counts, predict_proba recording Meaningless comparisons lead to false optimism in medical machine learning less depth
5 Sep 24 Pipelines & hyperparameter optimization recording and supplemental recording more depth
Dataset of the week: Predicting income from census data
6 Sep 29 Overfitting the validation set & encoding categorical variables recording more depth
7 Oct 1 Imputation, scaling numeric features, ColumnTransformer recording more depth
Dataset of the week: detecting credit card fraud
8 Oct 6 Evaluation metrics for classification recording Damage Caused by Classification Accuracy and Other Discontinuous Improper Accuracy Scoring Rules, Classification vs. Prediction more depth
9 Oct 8 Ensembles recording similar
Dataset of the week: predicting housing prices
10 Oct 13 Linear regression, regression metrics recording more depth on metrics, less on linear regression
11 Oct 15 Prediction intervals, feature importances recording new
12 Oct 20 Feature selection, midterm review recording Feature selection article less depth
Oct 22 MIDTERM study materials
13 Oct 27 Neural networks & computer vision recording But what is a Neural Network? less depth
14 Oct 29 Distances and neighbours recording less depth
15 Nov 3 Text data recording - note: unfortunately the first 9 min of the recording was corrupted new
16 Nov 5 Outliers recording different
17 Nov 10 Time series data recording Humour: The Problem with Time & Timezones new
18 Nov 12 Survival analysis recording Calling Bullshit video 4.1, Medium article (contains some math) new
19 Nov 17 Clustering recording less depth
20 Nov 19 Communicating your results recording Calling BS videos Chapter 1 (5 short videos, 37 min total); Communication in Data Science blog post new
Nov 19 Bonus material: SGD for big datasets recording less depth
21 Nov 24 Communicating your results, continued recording Calling BS videos Chapter 6 (6 short videos, 47 min total); Can you read graphs? Because I can't. by Sabrina (7 min) new
22 Nov 26 Ethics recording Calling BS videos Chapter 5 (6 short videos, 50 min total); Humour: Game of Thrones deepfake video; The ethics of data science new
Nov 26 Bonus material: combining multiple tables recording new
23 Dec 1 Ethics continued: guest lecture by Sina Fazelpour recording is available on Canvas only new
24 Dec 3 Model deployment; Conclusion recording new

Homework schedule

# Due Date Associated lectures
1 Tue Sep 15 11:59pm prerequisites
2 Mon Sep 21 11:59pm 2, 3
3 Mon Sep 28 11:59pm 4, 5
4 Mon Oct 12 11:59pm 6-9
5 Mon Oct 19 11:59pm 9-11
6 Mon Nov 9 11:59pm 12-15
7 Mon Nov 16 11:59pm 16-18
8 Mon Nov 23 11:59pm whole course
9 Mon Nov 30 11:59pm 20-22

Attribution

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

License

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

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