/cpsc330

CPSC 330: Applied Machine Learning

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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.

# 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 pw !?3niNc^ less depth
3 Sep 17 The fundamental tradeoff of ML recording pw 90p@qbt4 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 pw ZsJZ#e29 Meaningless comparisons lead to false optimism in medical machine learning less depth
5 Sep 24 Pipelines & hyperparameter optimization recording pw pT2QVE*# and supplemental screencast more depth
Dataset of the week: Predicting income from census data
6 Sep 29 Overfitting the validation set & encoding categorical variables recording pw 3J10UiO. more depth
7 Oct 1 Imputation, scaling numeric features, ColumnTransformer recording pw b.+DFR47 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 part 1, recording part 2 more depth on metrics, less on linear regression
11 Oct 15 Prediction intervals, feature importances recording new
12 Oct 20 Feature selection, midterm review screencast 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 less depth

Below this point is the schedule for future lectures (not yet updated for 2020W1)

# Date Topic Recording Related readings and links vs. CPSC 340
15 Nov 3 Feature engineering, embeddings new
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 Communicating your results Communication in Data Science blog post; Calling BS videos Chapter 1 (5 video total) new
21 Nov 24 Communicating your results, continued Calling BS videos Chapter 6 (6 short videos, 47 min total) new
22 Nov 26 Ethics Calling BS videos Chapter 5 (6 short videos, 50 min total) new
23 Dec 1 Model deployment 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
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.