Basics (1 lecture)
- What is learning?
- Point estimation and MLE
- Gaussians
Wed., 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/ - Bias-Variance tradeoff
- Overfitting
- Regularization
- Cross-validation
- LASSO
Mon., September 30:
- Lecture: Bayesian learning, linear regression, bias-variance tradeoff. [Slides] [Annotated Slides]
- Readings: Murphy 7.1-7.3, 6.4, 7.5.1
Tue., October :
- Optional: Python tutorial. LOW 101 5:30pm [Code and dataset]
Wed., October 2:
- Lecture: Overfitting, regularization, ridge regression, cross-validation. [Slides] [Annotated Slides]
- Readings: Murphy 6.5.3
Mon., October 7:
- Lecture: Variable selection, sparsity, LASSO. [Slides] [Annotated Slides]
- Readings: Murphy 13.1, 13.3-13.4.1
Tue., October 8:
- Recitation: Bias-Variance Trade-Off, L1 vs. L2. LOW 101 5:30pm [iPython Notebook] [static html]
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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
Wed., October 9:
- Lecture: Classification, logistic regression, gradient descent. [Slides] [Annotated Slides]
- Readings: Murphy 8.1 - 8.3, 8.5.2
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Non-linear 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/ - Instance-based learning
- K-nearest neighbors
- Kernels
Mon., October 14:
- Lecture: Stochastic gradient descent, boosting. [Boosting Slides] [Annotated Slides]
- Readings: Murphy 16.4
Wed., October 16:
- Lecture: Boosting continued, decision trees. [Decision Trees Slides] [Annotated Slides]
- Readings: Murphy 16.2
Mon., October 21:
- Lecture: Decision trees continued, instance-based learning, nearest neighbors. [Instance-based Learning Slides] [Annotated Slides]
Tue., October 22:
- Recitation: Boosting/evaluation . LOW 101 5:30pm [Slides]
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Online Learning and Margin-based 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]
Wed., October 23:
- Lecture: Online learning, perceptron, hinge loss. [Slides] [Annotated Slides]
- Readings: Murphy 8.5.0, 8.5.4, 14.4
Mon., October 28:
- Lecture: Kernels, SVMs. [Slides] [Annotated Slides]
- Readings: Murphy 14.5
Wed., October 30:
- IN CLASS - Midterm
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Unsupervised Learning (3 Lectures)
- K-means
[Applet: K-means]
http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html - Expectation Maximization (EM) for Mixture of Gaussians
- Dimensionality reduction (PCA, SVD) [Applet: PCA]
Mon., November 4:
- Lecture: Clustering, K-means. [Slides] [Annotated Slides]
- Readings: Murphy 11.4.2.5
Tue., November 5:
- Recitation: Perceptron, SVM. LOW 101 5:30pm. [Slides]
Wed., November 6:
- Lecture: Mixtures of Gaussians, EM. [Slides] [Annotated Slides]
- Readings: Murphy 11.2, 11.4 - 11.4.2.3
Mon., November 11:
- NO CLASS - Veterans Day
Wed., 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
Mon., November 18:
- Lecture: CANCELLED.
Wed., November 20:
- Lecture: PCA continued, Naive Bayes. [Slides] [Johan's Additional Notes]
- Readings: Murphy 3.5, 10.1 - 10.2.2
Mon., November 25:
- Lecture: Bayesian Networks, Bayes net structure learning. [Slides] [Annotated Slides]
- Readings: Murphy 10.3
Tue., November 26:
- Recitation: Naive Bayes and Bayesian Networks. LOW 101 5:30pm [Slides]
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Learning Theory (2 Lectures)
- Sample complexity
- PAC learning
- VC-dimension
Wed., November 27:
- Lecture: Bayes nets structure learning, learning Theory. [Learning Theory Slides] [Annotated Slides]
- Readings: Murphy 6.5.4
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Learning to Make Decisions (2 Lectures)
- Markov decision processes
- Reinforcement learning
Mon., December 2:
- Lecture: Learning theory continued, Markov decision processes. [MDPs Slides] [Annotated Slides]
Wed., December 4:
- Lecture: MDPs, Reinforcement learning. [Slides] [Annotated Slides]
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