|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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