CPSC 340 - Machine Learning 2020

Final

The Examination Document(s)

The Kaggle autonomous driving competition

Midterm

The Examination Document(s)

The Kaggle COVID-19 competitions

Syllabus and Schedule

# Date Lecture Related readings and links Homework and tutorials
1 Wed 09/09 Syllabus What is Machine Learning, Machine Learning, Rise of the Machines, Talking Machine Episode 1 a1 released
2 Fri 11/09 Exploratory data analysis Gotta Catch’em all, Why Not to Trust Statistics, Visualization Types, Google Chart Gallery, Other tools  
         
3 Mon 14/09 Decision trees A Visual Introduction to Machine Learning, Decision Trees, Entropy, AI:AMA 18.2-3, ESL: 9.2, ML:APP 16.2 Big-O Notes
4 Wed 16/09 Fundamentals of learning 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 Fri 18/09 Probabilistic Classifiers Conditional probability (demo), Naive Bayes, Probabilities and Battleship, ESL 4.3, ML: APP 2.2, 3.5, 4.1-4.2 a1 due, Probability Notes, Probability Slides
         
6 Mon 21/09 Non-parametric models K-nearest neighbours, Decision Theory for Darts, Norms, AI: AMA 18.8, ESL 13.3, ML:APP 1.4  
7 Wed 23/09 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  
8 Fri 25/09 Clustering Clustering, K-means clustering (demo), K-Means++ (demo), IDM 8.1-8.2, ESL: 14.3  
         
9 Mon 28/09 More clustering DBSCAN (video, demo), Hierarchical Clustering, Phylogenetic Trees, IDM 8.4  
10 Wed 30/09 Outlier Detection Empirical Study, IDM 8.3, ESL 14.3.12, ML:APP 25.5 a2 due
11 Fri 02/10 Least Squares 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 Calculus Notes
         
12 Mon 05/10 Nonlinear regression 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
13 Wed 07/10 Gradient descent Gradient Descent, Convex Functions  
14 Fri 09/10 Robust Regression ML:APP 7.4  
         
  Mon 12/10 Thanksgiving    
15 Wed 14/10 Feature Selection Genome-Wide Association Studies, AIC, BIC, ESL 3.3 , 7.5-7 a3 due
16 Fri 16/10 Regularization ESL 3.4., ML:APP 7.5, AI:AMA 18.4  
         
17 Mon 19/10 More Regularization RBF video, RBF and Regularization video, ESL 6.7, ML:APP 13.3-4 MIDTERM OUT
18 Wed 21/10 Linear Classifiers Perceptron, ESL 4.5, ML:APP 8.5  
19 Fri 23/10 More Linear Classifiers Support Vector Machines, ESL 4.4, 12.1-2, ML:APP 8.1-3, 9.5 14.5, AI:AMA 18.9  
         
20 Mon 26/10 Feature Engineering Gmail Priority Inbox MIDTERM DUE
21 Wed 28/10 Convolutions    
22 Fri 30/10 Kernel Trick ESL 12.3, ML:APP 14.1-4  
         
23 Mon 02/11 Stochastic Gradient Stochastic Gradient, ML:APP 8.5  
24 Wed 04/11 Boosting AdaBoost (video), XGBoost (video), ML:APP 16.4 Max and Argmax Notes
25 Fri 06/11 MLE and MAP Maximum Likelihood Estimation, ML:APP 9.3-4 a4 due
         
26 Mon 09/11 Principal Component Analysis Principal Component Analysis, ESL 14.5, IDM B.1, ML:APP 12.2  
  Wed 11/11 Remembrance Day    
27 Fri 13/11 More PCA Making Sense of PCA, SVD, Eigenfaces  
         
28 Mon 16/11 Sparse Matrix Factorization Non-Negative Matrix Factorization (original - access from UBC), ESL 14.6, ML: APP 13.8  
29 Wed 18/11 Recommender Systems Recommender Systems, Netflix Prize a5 due
30 Fri 20/11 Multi-Dimensional Scaling Nonlinear Dimensionality Reduction, t-SNE demo, ESL 14.8-9, IDM B.2  
         
31 Mon 23/11 Deep Learning Google Video, What is a Neural Network?, Interactive Guide, ML:APP 16.5, ESL 11.1-4, AI: AMA 18.7  
32 Wed 25/11 More Deep Learning Fortune Article, Deep Learning References, Alchemy  
33 Fri 27/11 Convolutional Neural Networks Convolutional Neural Networks, ML:APP 28.4, ESL 11.7  
         
34 Wed 02/12 Automatic Differentiation 1    
34 Mon 30/11 TBD   a6 due