Week | Date | Topic | Notes |
---|---|---|---|
1 | Feb 17 | Overview of Machine Learning, Your 1st classifier: Nearest Neighbor Classifier [slides] [4-per-page] | Reading: Barber 1,14.1-14.2 Video: What is Machine Learning?, Bernhard Schölkopf Demo: k-Nearest Neighbors |
2 | Feb 24 | Linear Regression, Least Squares [slides] [4-per-page] | PS1 out Reading: Bishop 1.1, 3.1, Stanford CS229 note Demo: Linear regression |
3 | Mar 3 | Machine Learning Methodology, Basic Probability and Linear Algebra Reviews [slides] [4-per-page] | Reading: Murphy 2.1-2.3.2, CIS 520 note, P. Domingos, A few useful things to know about machine learning, E. Simoncelli, A Geometric Review of Linear Algebra Video: Probability Primer |
4 | Mar 10 | Statistical Estimation: MLE, MAP, Naïve Bayes Classifier [slides] [4-per-page] | Reading: Barber 8.6, 8.7, Naïve Bayes, Tom M. Mitchell Optional Reading: Learning to Decode Cognitive States from Brain Images, Tom M. Mitchell et al. Video: Daphne Koller, Probabilistic Graphical Models, MLE Lecture, MAP Lecture |
5 | Mar 17 | Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron [slides] [4-per-page] | PS1 due, PS2 out Reading: Barber 17.4, Bishop 4.1.1-4.1.2, 4.5 Optional Reading: On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, Andrew Y. Ng, Michael I. Jordan |
6 | Mar 24 | Support Vector Machines (part 1)[slides] [4-per-page] | Reading: M.A. Hearst, Support Vector Machines, C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition |
7 | Mar 31 | Support Vector Machines (part 2) | |
8 | Apr 7 | Midterm exam | |
9 | Apr 14 | Decision Tree Learning [slides] [4-per-page] | PS2 due Reading: Mitchell 3, Bishop 14.4 |
10 | Apr 21 | Ensemble Methods: Bagging, Random Forests, Boosting [slides] [4-per-page] | PS3 out (beach and grassland images) Reading: Murphy 16.4, Boosting, Robert Schapire Optional Reading: Real-Time Human Pose Recognition in Parts from Single Depth Images, Jamie Shotton et al. Video: A Boosting Tutorial, Robert Schapire |
11 | Apr 28 | Clustering [slides] [4-per-page] | Reading: Bishop 9.1, A Tutorial on Spectral Clustering, U. von Luxburg Video: Clustering, GMM, Andrew Ng |
12 | May 5 | Deep Learning [slides] [4-per-page] | Reading: Mitchell 4, Bishop Ch. 5, A. Karpathy, Hacker's guide to Neural Networks Video: Neural Networks, Backpropagation, Andrew Ng> Demo: A toy neural network |
13 | May 12 | Principle Component Analysis | PS3 due |
14 | May 19 | Course wrap-up |
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