| 1 |
Jan 4 |
Syllabus |
Machine Learning, Rise of the Machines, Talking Machine Episode 1 |
Assignment 0 released |
| 2 |
Jan 6 |
Data Exploration |
Gotta Catch'em all, Why Not to Trust Statistics, Visualization Types, Google Chart Gallery, Other tools |
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| 3 |
Jan 9 |
Decision Trees |
Notes on big-O, A Visual Introduction to Machine Learning, Decision Trees, Entropy, What make Dr. Seuss so silly?, AI:AMA 18.2-3, ESL: 9.2, ML:APP 16.2 |
Tutorial: GitHub, Python, gradients |
| 4 |
Jan 11 |
Learning Theory |
IID, Cross-validation, Bias-variance, No Free Lunch, AI: AMA 18.4-5, ESL 7.1-7.4, 7.10, ML:APP 1.4, 6.5 |
Assignment 0 due |
| 5 |
Jan 13 |
Generative Models |
Notes on probability, Extra slides on probability, Conditional probability (demo), Naive Bayes, Notes on Naive Bayes, Probabilities and Battleship, ESL 4.3, ML: APP 2.2, 3.5, 4.1-4.2 |
Assignment 1 released |
| 6 |
Jan 16 |
Non-Parametric Models |
K-nearest neighbours, Decision Theory for Darts, AI: AMA 18.8, ESL 13.3, ML:APP 1.4 |
Tutorial: Python plotting, naive Bayes practice |
| 7 |
Jan 18 |
Ensemble Methods |
Ensemble Methods, Random Forests, Empirical Study, Kinect, AI: AMA 18.10, ESL: 7.11, 8.2, 15, 16.3, ML: APP 6.2.1, 16.2.5, 16.6 |
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| 8 |
Jan 20 |
Clustering |
Clustering, K-means clustering (demo), K-Means++ (demo), IDM 8.1-8.2, ESL: 14.3 |
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| 9 |
Jan 23 |
Density-based Clustering |
Bonus slides, DBSCAN (video, demo), Norms, IDM 8.4 |
Assignment 1 due (Sunday night), Tutorial: decision trees and random forests |
| 10 |
Jan 25 |
Hierarchical Clustering |
bonus slides, Hierarchical Clustering, Phylogenetic Trees, IDM 8.3, ESL 14.3.12, ML:APP 25.5 |
Assignment 2 released |
| 11 |
Jan 27 |
Outlier Detection |
Survey and Empirical Study, IDM 10.1-5 |
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| 12 |
Jan 30 |
Association Rules |
Association Rule Learning, Apriori, Amazon Product Recommendation, IDM 6.1-6.3, ESL 14.2 |
Tutorial: hw2 python code, vector quantization |
| 13 |
Feb 1 |
Linear Regression |
Linear Regression (demo, 2D data, 2D video) Least Squares, Partial Derivatives, Gradient, Notes on Linear Algebra, ESL 3.1-2, ML:APP 7.1-3, AI:AMA 18.6 |
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| 14 |
Feb 3 |
Non-Linear Regression |
bonus slides, Fluid simulation paper, Fluid simulation video, Linear/Quadratic Gradients, ESL 5.1, 6.3, and 6.7 |
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| 15 |
Feb 6 |
Regularization |
Bonus slides, in-class demo, RBF video, RBF and Regularization video, Stein's Paradox visualization, ESL 3.4, ML:APP 7.5, AI:AMA 18.4 |
hw2 due (Sunday night), Tutorial: hw3 practice problems, Assignment 3 released |
| 16 |
Feb 8 |
Gradient Descent |
Bonus slides, in-class demo, Gradient Descent, ML:APP 7.4 |
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| 17 |
Feb 10 |
Logistic Regression |
Gmail Priority Inbox, ESL 4.4, ML:APP 8.1-3, AI:AMA 18.9 |
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Feb 13 |
FAMILY DAY |
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Tutorials cancelled this week |
| 18 |
Feb 15 |
Support Vector Machines |
Support Vector Machines, ESL 4.5 and 12.1-2, ML:APP 14.5 |
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| 19 |
Feb 17 |
Kernel Methods |
in-class demo, ESL 12.3, ML:APP 14.1-4 |
hw3 due Sunday night |
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MIDTERM BREAK |
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| 20 |
Feb 27 |
Stochastic Gradient |
Stochastic Gradient, ML:APP 8.5 |
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Mar 1 |
MIDTERM EXAM |
Practice exams available on github.ubc.ca |
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Mar 3 |
Midterm solutions |
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hw4 released |
| 21 |
Mar 6 |
Feature Selection |
Bonus slides, ESL 3.3 |
Tutorial: hw4 code |
| 22 |
Mar 8 |
L1-Regularization & Maximum Likelihood |
Bonus slides, Maximum Likelihood Estimation, ESL 3.4, ML:APP 13.3-4 |
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| 23 |
Mar 10 |
Multi-Class Classification |
ML:APP 8.3.7 and 9.3-5, ESL 4.4 |
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| 24 |
Mar 13 |
Principal Component(s) Analysis |
Principal Component Analysis, ESL 14.5, IDM B.1, ML:APP 12.2 |
Tutorial: maximum likelihood |
| 25 |
Mar 15 |
More PCA |
SVD, Eigenfaces |
Assignment 5 released |
| 26 |
Mar 17 |
Sparse Matrix Factorization |
Non-Negative Matrix Factorization, ESL 14.6, ML: APP 13.8 |
hw4 due Sunday night |
| 27 |
Mar 20 |
Recommender Systems |
Recommender Systems, Netflix Prize |
Tutorial: Dimensionality reduction |
| 28 |
Mar 22 |
Nonlinear Dimensionality Reduction |
Nonlinear Dimensionality Reduction, t-SNE video, t-SNE caveats, ESL 14.8-9, IDM B.2 |
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| 29 |
Mar 24 |
Neural Networks |
in-class demo, Google Video, Fortune Article, great list of resources, ML:APP 16.5, ESL 11.1-4, AI: AMA 18.7 |
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| 30 |
Mar 27 |
Deep Learning |
in-class demo, bonus slides ML:APP 28.3, ESL 11.5 |
Tutorial: time will be used as extra office hours |
| 31 |
Mar 29 |
Convolutional Neural Networks |
bonus slides (more convolutions), Convolutional Neural Networks, AlexNet, ML:APP 28.4, ESL 11.7 |
Assignment 6 released |
| 32 |
Mar 31 |
More CNNs |
Artistic Style Transfer video, Deep Photo Style Transfer |
Assignment 5 due (11:59pm) |
| 33 |
Apr 3 |
Deep learning software |
If you want to follow along, sign up for an AWS account before class. You can try for an Amazon Educate student account but we've still working out some issues with this. |
Tutorial: deep learning / hw6 |
| 34 |
Apr 5 |
Conclusion |
We'll do the course/TA evaluations during this lecture as well. |
Assignment 6 due (11:59pm on Friday April 7) |