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Date |
Topics |
Readings |
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Introduction |
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M 8/30 |
Introduction Overview, applications, history |
Bishop 1 |
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W 9/1 |
Math Review Calculus, linear algebra, optimization |
Linear algebra Bishop Appendix C |
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M 9/6 |
Labor Day No Class |
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W 9/8 |
Probability Probability, stats |
You do NOT need to read all Probability: Bishop Chapter 2 Bishop Appendix B |
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M 9/13 |
Machine Learning Foundations Learning definitions, settings |
Bishop 1 |
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W 9/15 |
Decision Trees Construction, pruining, over-fitting |
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Supervised Learning: Linear Methods |
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M 9/20 |
Regression Least squares and regression |
Bishop 3 |
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W 9/22 |
Classification Logistic Regression |
Bishop 4 |
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M 9/27 |
Generative vs. discriminative Naive Bayes and Logistic Regression |
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W 9/29 |
Online methods Perceptron, multi-class, structured |
Blum. On-Line Algorithms in Machine Learning. 1996 Bishop 4.1.2 Reducing Multiclass to Binary (Sections 1, 2, 3) |
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Supervised Learning: Non-Linear Methods |
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M 10/4 |
Support Vector Machines Max-margin classification and optimization |
Bishop 7.1 Bishop Appendix E |
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W 10/6 |
Kernel Methods Dual optmization, kernel trick |
Bishop 6.1, 6.2 |
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Tue 10/12 |
Instance based learning Nearest-neighbors |
Bishop 2.5 Mitchell 8-8.4 |
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W 10/13 |
Neural Networks Neural Network models |
Bishop 5.1,5.2,5.3,5.5 |
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M 10/18 |
Ensemble Methods Boosting |
Bishop 14.1,14.2,14.3 |
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W 10/20 |
Current Trends in Supervised Learning |
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Unsupervised Learning |
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M 10/25 |
Clustering Expectation-Maximization and k-means |
Bishop 9 |
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W 10/27 |
EM and Clustering 1 Gaussian mixture models |
Bishop 9 |
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M 11/1 |
EM and Clustering 2 The EM Algorithm |
Bishop 9 |
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Graphical Models |
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W 11/3 |
Graphical models 1 Bayesian networks and conditional independence |
Bishop 8.1, 8.2 |
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M 11/8 |
Graphical models 2 MRFs and Exact inference |
Bishop 8.3, 8.4 |
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W 11/10 |
Graphical models 3 Inference |
Bishop 8.3, 8.4 |
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M 11/15 |
Sequential graphical models 1 Max Sum and Max Product |
Bishop 13.1,13.2 |
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W 11/17 |
Sequential graphical models 2 HMMs and CRFs |
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Other Topics |
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M 11/22 |
Dimensionality reduction PCA, probabilistic PCA, LDA |
Bishop 12.1,12.2,12.3 |
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W 11/24 |
Thanksgiving Break No class |
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M 11/29 |
Guest Lecture: Current Trends in Unsupervised Learning Large-scale nonparametric methods |
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W 12/1 |
Semi-supervised learning Guest Lecture |
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M 12/13 9am |
Final Poster Session Time Project presentations |
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