/CPSC340-2021W2

Course webpage for 340 (Machine Learning) for 2021 W2 (taught in Jan-Apr 2022)

Primary LanguageJupyter Notebook

CPSC 340: Machine Learning and Data Mining (2021W2)

This is the public-facing portion of the course website; also see the Canvas course for links to the course-specific pages for Piazza, Gradescope, lecture recordings, and submitting assignments.

Course documents

Schedule

Note: In the timetable below, the textbook codes (such as "AI:AMA") are defined here.

# Date Slides Instructor Related Readings and Links Homework and Notes
1 Mon Jan 10 Motivation and Syllabus Both What is Machine Learning? Machine Learning
Rise of the Machines Talking Machine Episode 1		 a1 posted
2 Wed Jan 12 Exploratory Data Analysis Jeff Companion notebook, Gotta Catch'em all Why Not to Trust Statistics
Visualization Types Google Chart Gallery Other Tools
3 Fri Jan 14 Decision Trees Jeff A Visual Introduction to Machine Learning, Decision Trees Entropy
AI:AMA 18.2-3, ESL: 9.2, ML:APP 16.2		 Big-O Notes, Week 1 Tutorials
4 Mon Jan 17 Fundamentals of Learning Jeff Companion notebook, 7 Steps of Machine Learning 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		 Course Notation Guide
5 Wed Jan 19 Probabilistic Classifiers Mi Jung Conditional probability (demo) Naive Bayes Probabilities and Battleship
ESL 4.3, ML: APP 2.2, 3.5, 4.1-4.2		 Assignment 1 due
a1 solutions
Probability Notes Probability Slides
6 Fri Jan 21 Non-Parametric Models Mi Jung Companion notebook, K-nearest neighbours Decision Theory for Darts Norms
AI: AMA 18.8, ESL 13.3, ML:APP 1.4		 Assignment 2 posted
Week 2 Tutorials
7 Mon Jan 24 Ensemble Methods Jeff Companion notebook, 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
8 Wed Jan 26 Clustering Jeff Companion notebook, Clustering K-means clustering (demo) K-Means++ (demo)
IDM 8.1-8.2, ESL: 14.3
9 Fri Jan 28 More Clustering Jeff Companion notebook, DBSCAN (video, demo) Hierarchical Clustering Phylogenetic Trees
IDM 8.4		 Week 3 Tutorials
10 Mon Jan 31
Outlier Detection Jeff Empirical Study
IDM 8.3, ESL 14.3.12, ML:APP 25.5
11 Wed Feb 2
 Least Squares Mi Jung Companion notebook, Linear Regression (demo, 2D data, 2D video) Least Squares Essence of Calculus Partial Derivative Gradient
ESL 3.1-2, ML:APP 7.1-3, AI:AMA 18.6
12 Fri Feb 4
Nonlinear Regression Jeff Why should one learn machine learning from scratch? Essence of Linear Algebra Matrix Differentiation Fluid Simulation (video)
ESL 5.1, 6.3		 Linear Algebra Notes
Linear/Quadratic Gradients, Week 4 Tutorials, Assignment 2 due, Assignment 3 released
13 Mon Feb 7
Gradient Descent Jeff Companion notebook, Gradient Descent Convex Functions a2 solutions
Wed Feb 9
Guest Lecture on reinforcement learning Helen Zhang (12pm). Ben Norman (2pm) Helen Zhang slides, Ben Norman slides
14 Fri Feb 11
Robust Regression Mi Jung Companion notebook, ML:APP 7.4 Week 5 Tutorials
15 Mon Feb 14
Feature Selection Mi Jung Genome-Wide Association Studies AIC, BIC
ESL 3.3 , 7.5-7		 Assignment 3 due
a 3 solutions
16 Wed Feb 16
Regularization Mi Jung Companion notebook, ESL 3.4., ML:APP 7.5, AI:AMA 18.4
Feb 17 (6 - 7:30 pm)
MIDTERM
17 Fri Feb 18
More Regularization Mi Jung Companion notebook, RBF video RBF and Regularization video
ESL 6.7, ML:APP 13.3-4
Feb 21 -- 25
MIDTERM BREAK a4 posted (Feb 25)
18 Mon Feb 28
 Linear Classifiers Mi Jung Companion notebook, Perceptron
ESL 4.5, ML:APP 8.5
19 Wed Mar 2
More Linear Classifiers Mi Jung Companion notebook, Support Vector Machines
ESL 4.4, 12.1-2, ML:APP 8.1-3, 9.5 14.5, AI:AMA 18.9		 Week 6 Tutorials
20 Fri Mar 4
 Feature Engineering Jeff Gmail Priority Inbox
21 Mon Mar 7
Kernel Trick Mi Jung Companion notebook 1, Companion notebook 2, Companion notebook 3 ESL 12.3, ML:APP 14.1-4
22 Wed Mar 9
Stochastic Gradient Jeff Companion notebook, Stochastic Gradient
ML:APP 8.5		 Week 7 Tutorials
23 Fri Mar 11
Boosting, start of MLE Mi Jung AdaBoost (video) XGBoost (video)
ML:APP 16.4		 a4 due, a5 posted, Max and Argmax Notes
24 Mon Mar 14
MLE and MAP Mi Jung Companion notebook, Maximum Likelihood Estimation
ML:APP 9.3-4
25 Wed Mar 16
Principal Component Analysis Mi Jung Companion notebook, Principal Component Analysis
ESL 14.5, IDM B.1, ML:APP 12.2
26 Fri Mar 18
More PCA Mi Jung Companion notebook, Making Sense of PCA SVD Eigenfaces Week 8 Tutorials
27 Mon Mar 21
Sparse Matrix Factorization Mi Jung Companion notebook, Non-Negative Matrix Factorization (original - access from UBC)
ESL 14.6, ML: APP 13.8
28 Wed Mar 23
Recommender Systems Mi Jung Recommender Systems, Netflix Prize, Nonlinear Dimensionality Reduction, t-SNE demo, ESL 14.8-9, IDM B.2
29 Fri Mar 25
Deep Learning Jeff Companion notebook, Google Video What is a Neural Network? Interactive Guide
ML:APP 16.5, ESL 11.1-4, AI: AMA 18.7		 Assignment 5 due, a6 posted, Week 9 Tutorials
30 Mon Mar 28
More Deep Learning Jeff Fortune Article Deep Learning References, Alchemy
ML:APP 28.3, ESL 11.5
31 Wed Mar 30
Convolutions Jeff Companion notebook
32 Fri Apr 1
Convolutional Neural Networks Jeff Companion notebook, Convolutional Neural Networks
ML:APP 28.4, ESL 11.7		 Week 10 Tutorials
33 Mon Apr 4
 More CNNs Jeff
Wed Apr 6
 AI Neuroscience (and bonus material) Jeff
34 Fri Apr 8
 Conclusion both Assignment 6 due