CPSC 330: Applied Machine Learning In this repository you will find the course materials from the inaugural offering for CPSC 330: Applied Machine Learning at the University of British Columbia, which took place Jan-Apr 2020. I learned many lessons during the first run, so these materials definitely represent a work in progress.
Instructor: Mike Gelbart
Thank you to Tomas Beuzen and Varada Kolhatkar for significant contributions to the course materials.
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Topic
Related readings and links
vs. CPSC 340
1
Course intro, Python Python videos and notebooks
n/a
2
More Python: numpy and pandas Numpy quickstart tutorial , Learn python3 in Y minutes new
3
Decision trees Assumed preparation : Decision tree video until 26:30, and then continue from 36:35 onwards.less math
4
Fundamentals of learning Assumed preparation :
5
Logistic regression, feature extraction no video
less depth on log reg, more on features
6
Feature preprocessing, SVMs, random forests no video
more depth on features, less on SVM/RF
7
Model comparisons, EDA, missing data, baselines 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
Evaluation metrics for binary classification, hyperparameter optimization 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
Regression more depth on error metrics
10
Linear regression, feature importances more depth on feature importances, less on linear regresion
11
Ensembles, midterm review n/a
12
Pipelines, feature selection Feature selection article pipelines are new, less depth on feature selection
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Natural language processing new
14
Neural networks & computer vision But what is a Neural Network? less depth
15
Nearest neighbours, product similarity less depth
16
Time series data Humour: The Problem with Time & Timezones
new
17
Survival analysis Calling Bullshit video 4.1 , Medium article (contains some math)new
18
Clustering less depth
19
Outliers different angle
20
Miscellaneous leftovers new
21
Communicating your results Communication in Data Science blog post; Calling BS videos Chapter 1 (5 video total)new
22
Communicating your results, continued Calling BS videos Chapter 6 (6 short videos, 47 min total)new
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Ethics, course conclusion Calling BS videos Chapter 5 (6 short videos, 50 min total)new
See here .
See here .
Creative Commons Attribution-ShareAlike 4.0 International License .
