CS760, Fall 2017
Department of Computer Sciences
University of Wisconsinâ€“Madison
This schedule is tentative and subject to change. Please check back often.
The reading is not required but strongly recommended for all students. Those explicitely noted as optional are for students interested in that specific topic. "A, B; C; D" means to read (A OR B) AND C AND D. Text in red means a link to the reading material. Abbreviations for textbooks:
Date | Lecture | Readings | Homework Released | Homework Due |
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Wed, Sep 6 | course overview [Slides] | Homework 1 (background test) | ||
Fri, Sep 8 | machine learning overview [Slides] | Mitchell chapter 1, Murphy chapter 1; Dietterich, Nature Encyclopedia of Cognitive Science, 2003; Jordan and Mitchell, Science, 2015 |
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Mon, Sep 11 | decision tree learning part 1 [Slides] | Mitchell chapter 3, Murphy chapter 16.2, Shalev-Shwartz and Ben-David chapter 18 | ||
Wed, Sep 13 | decision tree learning part 2 [Slides] | Optional papers to read: 1. CART paper: Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 978-0-412-04841-8. 2. ID3 paper: Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81â€“106 3. C4.5 paper: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993. |
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Fri, Sep 15 | instance-based methods [Slides] | Mitchell chapter 8, Shalev-Shwartz and Ben-David chapter 19 | ||
Mon, Sep 18 | evaluating learning algorithms part 1 [Slides] | Mitchell chapter 5, Murphy 5.7.2, 6.5.3; Manning et al., Sections 8.3-8.4 |
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Wed, Sep 20 | evaluating learning algorithms part 2 [Slides] | Homework 1 due | ||
Fri, Sep 22 | no class | |||
Mon, Sep 25 | Naive Bayes [Slides] | Mitchell 6.1-6.10, Murphy 3 | ||
Wed, Sep 27 | regression (linear and losgistic) [Slides] | Murphy 8.1-3 and 8.6; Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training |
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Fri, Sep 29 | neural networks part 1 [Slides] | Mitchell chapter 4, Murphy 16.5 and 28, Bishop 5.1-5.3, Goodfellow-Bengio-Courville 6; LeCun et al., Nature, 2015 |
Homework 2 | |
Mon, Oct 2 | neural networks part 2 [Slides] | Mohri-Rostamizadeh-Talwalkar Appendix B (Convex Optimization), Bishop Appendix E (Lagrange Multipliers); Goodfellow-Bengio-Courville chapter 7 and 8 |
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Wed, Oct 4 | neural networks part 3 [Slides] | Goodfellow-Bengio-Courville chapter 9; Optional papers to read: 1. LeNet 2. AlexNet 3. ResNet |
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Fri, Oct 6 | neural networks part 4 [Slides] | Goodfellow-Bengio-Courville chapter 10; Optional papers to read: 1. LSTM 2. GRU |
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Mon, Oct 9 | neural networks part 5 [Slides] | Optional papers to read: 1. Deep Boltzmann Machines (DBM) 2. Generative Adversarial Networks (GAN) |
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Wed, Oct 11 | learning theory part 1: PAC model [Slides] | Mohri-Rostamizadeh-Talwalkar Chapter 2, Mitchell chapter 7 | ||
Fri, Oct 13 | learning theory part 2: mistake-bound model [Slides] | Optional paper to read: 1. Littlestone, N.; Warmuth, M. (1994). The Weighted Majority Algorithm. Information and Computation. |
Homework 3 | Homework 2 due |
Mon, Oct 16 | learning theory part 3: bias-variance decomposition [Slides] | Geman et al., Neural Computation, 1992 (Sections 1-3) | ||
Wed, Oct 18 | Bayesian networks part 1 [Slides] | Mitchell chapter 6, Bishop chapter 8.1, Shalev-Shwartz and Ben-David chapter 24 | ||
Fri, Oct 20 | Bayesian networks part 2 [Slides] | Heckerman Tutorial | ||
Mon, Oct 23 | Bayesian networks part 3 [Slides] | Optional paper to read: 1. TAN algorithm (Friedman et al., Machine Learning, 1997) 2. Sparse Candidate algorithm (Friedman, Nachman, and Peer, UAI, 1999) |
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Wed, Oct 25 | discriminative vs. generative learning | |||
Fri, Oct 27 | support vector machines part 1 | Homework 3 due | ||
Mon, Oct 30 | support vector machines part 2 | |||
Wed, Nov 1 | ensemble methods | |||
Fri, Nov 3 | feature selection | |||
Mon, Nov 6 | dimension reduction | |||
Wed, Nov 8 | reinforcement learning part 1 | |||
Fri, Nov 10 | reinforcement learning part 2 | |||
Mon, Nov 13 | machine learning in practice | |||
Wed, Nov 15 | active learning and semi-supervised learning | |||
Dec 15-Dec 21 | exam days |