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
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 .
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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 corruptednew
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 postnew
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 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
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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
Thank you to Tomas Beuzen and Varada Kolhatkar for significant contributions to the course materials.
Creative Commons Attribution-ShareAlike 4.0 International License .
