Basics (1 lecture)
 What is learning?
 Point estimation and MLE
 Gaussians
Thu., September 25:
 Lecture: What is learning? Point estimation and MLE, Gaussians. [Slides] [Annotated Slides]
 Additional Reference: Andrew Moore's basic probability tutorial
 Readings: Murphy 1.1  1.4, 2.4.1
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Linear Regression, Overfitting, Regularization, Sparsity (4 Lectures)
 Linear regression
[Applet]
http://mste.illinois.edu/users/exner/java.f/leastsquares/  BiasVariance tradeoff
 Overfitting
 Regularization
 Crossvalidation
 LASSO
Tue., September 30:
 Lecture: Linear regression, biasvariance tradeoff. [Slides] [Annotated Slides]
 Readings: Murphy 7.17.3, 6.4, 7.5.1
Wed., October 1:
 Optional: Python tutorial. EEB 045 5:00pm [Code and dataset]
Thu., October 2:
 Lecture: Overfitting, regularization, ridge regression, crossvalidation. [Slides] [Annotated Slides]
 Readings: Murphy 6.5.3
Tue., October 7:
 Lecture: Variable selection, sparsity, LASSO. [Slides] [Annotated Slides]
 Readings: Murphy 13.1, 13.313.4.1
Wed., October 8:
 Recitation: Bias/Variance, Linear Regression, LASSO. EEB 045 5:00pm [Bias/Variance Demo (Python)]
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Classification, Logistic Regression (1 Lecture)
 Logistic regression
[Applet]
http://www.cs.technion.ac.il/~rani/LocBoost/  Gradient descent, stochastic gradient descent
Thu., October 9:
 Lecture: Classification, logistic regression, gradient descent. [Slides] [Annotated Slides]
 Readings: Murphy 8.1  8.3, 8.5.2
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Nonlinear Models (3 Lectures)
 Decision trees
[Applet]
http://webdocs.cs.ualberta.ca/~aixplore/learning/DecisionTrees/Applet/DecisionTreeApplet.html  Boosting
[Adaboost Applet]
http://cseweb.ucsd.edu/~yfreund/adaboost/  Instancebased learning
 Knearest neighbors
 Kernels
Tue., October 14:
 Lecture: Stochastic gradient descent, boosting. [Boosting Slides] [Annotated Slides]
 Readings: Murphy 16.4
Wed., October 15:
 Recitation: Adaboost, Logistic Regression. EEB 045 5:00pm
Thu., October 16:
 Lecture: Decision trees. [Decision Trees Slides] [Annotated Slides]
 Readings: Murphy 16.2
Tue., October 21:
 Lecture: Decision trees, continued. Instancebased learning, nearest neighbors. [Instancebased Slides] [Instancebased Annotated Slides] [Decision Trees continued, Annotated Slides]
Wed., October 22:
 Recitation: Introduction to Boosted Trees, EEB 045, 5:00pm [Slides]
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Online Learning and Marginbased Approaches (2 Lectures)
 Perceptron
 Kernel trick
 SVMs

[LibSVM Applet]
http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 
[Another SVM Applet]
http://svm.dcs.rhbnc.ac.uk/pagesnew/GPat.shtml

[LibSVM Applet]
Thu., October 23:
 Lecture: Online learning, perceptron, hinge loss. [Slides] [Annotated Slides]
 Readings: Murphy 8.5.0, 8.5.4, 14.4
Tue., October 28:
 Lecture: Kernels, SVMs. [Slides] [Annotated Slides]
 Readings: Murphy 14.5
Wed., October 22:
 Recitation: Decision Trees, Midterm Review, EEB 045, 5:00pm
Thu., October 30:
 IN CLASS  Midterm
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Unsupervised Learning (3 Lectures)
 Kmeans
[Applet: Kmeans]
http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html  Expectation Maximization (EM) for Mixture of Gaussians
 Dimensionality reduction (PCA, SVD) [Applet: PCA]
Tue., November 4:
 Lecture: SVMs, continued. Clustering, Kmeans. [Slides] [Annotated SVMs Slides] [Annotated Clustering Slides]
 Readings: Murphy 11.4.2.5
Wed., November 5:
 Recitation: SVM [Slides]
Thu., November 6:
 Lecture: Mixtures of Gaussians, EM. [Slides] [Annotated Slides]
 Readings: Murphy 11.2, 11.4  11.4.2.3
Tue., November 11:
 NO CLASS  Veterans Day
Wed., November 12:
 Recitation: Kmeans/GMM, EEB 045 5:00pm
Thu., November 13:
 Lecture: EM Continued, dimensionality reduction, PCA, SVD. [Slides] [Annotated Slides]
 Readings: Murphy 12.1.0, 12.2
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Structured Models (2 Lectures)
 Naive Bayes
[Applet]
http://www.cs.technion.ac.il/~rani/LocBoost/  Graphical Models

[Applet: Java Bayes]
http://sites.poli.usp.br/pmr/ltd/Software/javabayes/Home/ 
[Another Bayes net Applet]
http://www.aispace.org/bayes/version5.1.6/bayes.jnlp  Representation
 Parameter and Structure Learning
Tue., November 18:
 Lecture: Naive Bayes, Bayesian Networks. [Slides] [Annotated Slides]
 Readings: Murphy 3.5, 10.1  10.2.2
Thu., November 20:
 Lecture: Bayesian Networks continued, Bayes net structure learning. [Slides] [Annotated Slides]
 Readings: Murphy 10.3
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Learning Theory (2 Lectures)
 Sample complexity
 PAC learning
 VCdimension
Tue., November 25:
 Lecture: Bayes nets structure learning, continued. Learning Theory. [Learning Theory Slides] [Annotated Structure Learning Slides] [Annotated Learning Theory Slides]
 Readings: Murphy 6.5.4
Wed., November 26:
 Recitation: No recitation
Thu., November 27:
 NO CLASS  THANKSGIVING
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Neural networks, deep learning (1 Lecture)
 Neural network
 Deep architectures
 Convolutional models
 Dropouts
Tue. December:
 Lecture: Neural networks, convolutional neural networks for computer vision. [Slides]
Learning to Make Decisions (1 Lecture)
 Markov decision processes
 Reinforcement learning
Thu., December 4:
 Lecture: Markov decision processes, Reinforcement learning. [Slides] [Annotated Slides]
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