Fall 2010
Machine Learning home page | Syllabus | Schedule/Readings | Project Assignments | Resources |
Date | Topics | Assigned Readings |
Thurs. 8/19 |
Course Introduction Intro. to Learning |
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Tues. 8/24 |
Designing learning systems (No lecture slides, but see Mitchell's Ch. 1 slides for related material, especially slides 11-21.) |
Ch. 1 of Mitchell or Ch. 1 of Nilsson |
Thurs. 8/26 |
Neural Networks Introduction Perceptrons Gradient descent (No lecture slides, but see Mitchell's Ch. 4 slides for related material.) |
Ch. 4 of Mitchell or Ch. 4 of Nilsson "The Basic Ideas in Neural Networks", by Rumelhart et al., Communications of the ACM, 37(3): 87-92, 1994. |
Tues. 8/31 |
Neural Networks (con't.) Stochastic gradient descent Multi-layer feedforward neural networks |
Ch. 4 of Mitchell or Ch. 4 of Nilsson Handout: Notes on Neural Networks
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Thurs. 9/2 |
Neural Networks (con't.) Back Propagation Convergence issues K-fold cross-validation |
Ch. 4. of Mitchell or Ch. 4 of Nilsson |
Tues. 9/07 | Neural Networks (con't.) Momentum Design and performance issues Discussion of project #1 |
Ch. 4 of Mitchell or Ch. 4 of Nilsson Project 1 assigned |
Thurs. 9/09 | Neural Networks (con't.) Case studies
Evaluation of learning algorithms: |
Case Studies: A case study of using artificial neural networks for classifying cause of death from verbal autopsy, International Journal of Epidemiology, 2001, 30:515-520.
Predicting Students' Academic Performance using Artificial Neural Networks: A Case Study of an Engineering Course, The Pacific Journal of Science and Technology, Vol. 9, No. 1, May-June 2008, pgs. 72-79. A case study on using neural networks to perform technical forecasting of forex, Neurocomputing, Vol. 24, pgs. 79-98, 2000. Mangerial applications of neural networks: The Case of Bank Failure Predictions, Management Sceince, Vol. 38, No. 7, July 1992, pgs. 926-947. |
Tues. 9/14 | Bias in machine learning Genetic Algorithms Introduction Representation Crossover operators Evolutionary programming example: GOLEM project for evolving robots (No lecture slides, but see Mitchell's Ch. 9 slides for related material.) |
Ch. 9.1-9.2 of Mitchell
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Thurs. 9/16 | Genetic Algorithms Selection/Fitness TSP example
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Ch. 9.2-9.3 of Mitchell
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Tues. 9/21 | Genetic Programming Introduction Example: Boolean 11-Multiplexer |
Ch. 9.5 of Mitchell Extra handouts: excerpts from John Koza's Genetic Programming, parts of Ch. 6 (pgs. 79-94) and Ch. 7 (pgs. 147-187) |
Thurs. 9/23 | Genetic Programming Closure and Sufficiency Generating initial population Example: Santa Fe Ant |
Koza handouts from last class
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Tues. 9/28 | Genetic Programming Example: Symbolic regression Example: Blocks world Dealing with "code bloat" Revisiting GOLEM example
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Automatic design and manufacture of robotic lifeforms, Nature,
vol. 406, August 31, 2000, pg. 974-978. |
Thurs. 9/30 |
Discussion of Project #2 Introduction to Reinforcement Learning |
Project #2 Assigned
Ch. 13.1 - 13.2 of Mitchell Ch. 1 of Sutton and Barto text on RL |
Tues. 10/05 | In-class design exercise (no lecture; instructor on travel) |
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Thurs. 10/07 | Fall break, no class |
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Tues. 10/12 | Reinforcement learning Markov Property Markov Decision Processes K-Armed Bandit example Q-Learning |
Ch. 13.3 of Mitchell Ch. 3, 6.5 of Sutton and Barto text on RL |
Thurs. 10/14 | Reinforcement learning TD Learning Sarsa (lambda) Eligibility traces |
Ch. 13.4 - 13.5 of Mitchell Ch. 6 and 7 of Sutton and Barto text on RL |
Tues. 10/19 | (Guest lecture by GTA; instructor on research travel) Tutorial on Player/Stage Reinforcement learning (con't.) Epsilon greedy action selection RL in-class exercise | Paper introducing Player Player/Stage Getting Started Guide (UTK-specific) Player/Stage Documentation (Public-domain website) Mini-Assignment #1 Assigned (easy!!) |
Thurs. 10/21 | (Guest lecture by GTA; instructor on research travel) Reinforcement learning (con't.) Case Studies: TD-Gammon Checkers Elevator Dispatch |
Ch. 11 of Sutton and Barto text on RL |
Tues. 10/26 | Discussion of Project #3 Wrapup of Reinforcement Learning
Introduction to Support Vector Machines (SVMs) |
Handout: pages 744-748 of Artificial Intelligence, 3rd edition, by Russell and Norvig, Prentice-Hall, 2010. Project #3 assigned |
Thurs. 10/28 | SVMs (con't.) Intro to convex optimization Intro to quadradic programming Intro to concepts of primal/dual SVM dual representation |
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Tues. 11/02 | SVMs (con't.) Maximizing margin formulation |
Handout: pages 73-87 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009. |
Thurs. 11/04 | SVMs (con't.) Nonlinear SVMs Kernels and the Kernel Trick Soft margin classifiers |
Handout: pages 102-109, 114-122 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009. |
Tues. 11/09 | No lecture; class optional Q&A on Project #3, led by GTA |
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Thurs. 11/11 | SVMs (con't.) Multi-Class Classification Regression with SVMs |
Handout: pages 185-202 Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009. |
Tues. 11/16 | SVMs (con't.) Practical issues: Categorical features Data scaling Missing data Model selection (use RBFs) Parameter selection using cross-validation and grid search AdaBoosting and connection to SVMs AdaBoosting with SVMs for unbalanced data |
Handout: A practical guide to support vector classification, by Hsu, et al. Handout: A Short Introduction to Boosting, by Freund and Schapire Handout: AdaBoost with SVM-based component classifiers, by Li, Wang, and Sung
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Thurs. 11/18 | AdaBoosting with SVMs for unbalanced data (con't.) Introduction to LIBSVM Discussion of Project #4 |
LIBSVM website Handout: AdaBoost.M1 description, by Freund and Schapire Project #4 assigned |
Tues. 11/23 | Stable/unstable learners Inductive bias Combining learners -- bagging |
Background reading: A simple
algorithm for learning stable machines, by Andonova, et al. |
Thurs. 11/25 | Happy Thanksgiving! (no class) |
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Tues. 11/30 | (Last class; no final exam) No Free Lunch theorem Combining learners (con't.) Course wrapup |
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