CS425/528: Schedule/Readings/Notes

CS425/528: Machine Learning

Schedule/Readings/Notes
Fall 2012

Subject to change. Check back frequently for updates.
Last updated: November 27, 2012

Date Topics Assigned Readings

Thurs. 8/23 Course Introduction
Intro. to Learning
Lecture slides

Tues. 8/28 Designing learning systems

(No lecture slides, but see Mitchell's Ch. 1 slides for related material, especially slides 11-21.)
Ch. 1

Thurs. 8/30 Neural Networks
Introduction
Perceptrons
Gradient descent

(No lecture slides, but see Mitchell's Ch. 4 slides for related material.)
Ch. 4 of Mitchell

"The Basic Ideas in Neural Networks", by Rumelhart et al., Communications of the ACM, 37(3): 87-92, 1994.

Tues. 9/04 Neural Networks (con't.)
Stochastic gradient descent
Multi-layer feedforward neural networks
Ch. 4 of Mitchell

Handout: Notes on Neural Networks

Thurs. 9/06 Neural Networks (con't.)
Back Propagation
Convergence issues
K-fold cross-validation
Ch. 4. of Mitchell

Tues. 9/11 Neural Networks (con't.)
Momentum
Design and performance issues

Exploring ANN design with Sharky
Discussion of project #1
Ch. 4 of Mitchell
Project 1 assigned

Thurs. 9/13 Neural Networks (con't.)
Case studies
Evaluation of learning algorithms:
Sensitivity, specificity
Type I and Type II errors
Confusion matrix
ROC curve

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.

Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets, by Gorman and Sejnowski, Neural Networks, Vol. 1, pgs. 75-89, 1988.
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. 9/18 Tutorial on LaTex

Genetic Algorithms
   Introduction

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

Thurs. 9/20 Genetic Algorithms
   Representation
   Crossover operators
   Selection/Fitness
   TSP example

Ch. 9.2-9.3 of Mitchell

Tues. 9/25 Genetic Programming
   Introduction
   Example: Boolean 11-Multiplexer
   Generating initial population
   Dealing with "code bloat"
Ch. 9.4-9.5 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. 9/27 Genetic Programming
   Closure and Sufficiency
   Example: Blocks world
   Example: Santa Fe Ant
Koza handouts from last class

Tues. 10/02 Genetic Programming
   Example: Symbolic regression

Revisiting GOLEM example
Discussion of Project #2

Automatic design and manufacture of robotic lifeforms, Nature, vol. 406, August 31, 2000, pg. 974-978.
Project #2 Assigned

Thurs. 10/04 Introduction to Reinforcement Learning
   Markov Property
   Markov Decision Processes

Ch. 13.1 - 13.2 of Mitchell
Ch. 1 of Sutton and Barto text on RL

Tues. 10/09 Guest lecture by TA; instructor on research travel Reinforcement learning
   Examples: Maze, K-Armed Bandit, etc.
   Infinite horizon, finite horizon
   V and Q functions Ch. 13.3 of Mitchell
Ch. 3, 6.5 of Sutton and Barto text on RL

Thurs. 10/11 Fall break, no class

Tues. 10/16 Review of Project #1
Reinforcement learning
  Q-Learning
  Non-deterministic Q-learning
  Conditions for convergence
  Epsilon greedy action selection

Ch. 13.4 - 13.5 of Mitchell
Ch. 6 and 7 of Sutton and Barto text on RL

Thurs. 10/18 Reinforcement learning
   TD Learning
   Sarsa (lambda)
   Eligibility traces
Tutorial on Player/Stage
Discussion of Mini-Assignment #1
Discussion of Project #3

Paper introducing Player
Player/Stage Getting Started Guide (UTK-specific)
Player/Stage Documentation (Public-domain website)
Mini-Assignment #1 Assigned (easy!!)

Project #3 assigned

Tues. 10/23 Guest lecture by TA; instructor on research travel
Reinforcement learning (con't.)
   Case Studies:
     TD-Gammon
     Checkers
     Elevator Dispatch

Ch. 11 of Sutton and Barto text on RL

Thurs. 10/25 UTK Engineer's Day (undergrad engineering students are dismissed if you need to be involved in Engr. Day activities)
Exercise overseen by TA; instructor on research travel
In-class ML design exercise (non-credit)

Tues. 10/30 Introduction to Support Vector Machines (SVMs)
Handout: pages 744-748 of Artificial Intelligence, 3rd edition, by Russell and Norvig, Prentice-Hall, 2010.

Thurs. 11/01 SVMs (con't.)
   Maximizing margin formulation
   Intro to convex optimization
Handout: pages 73-87 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.

Tues. 11/06 SVMs (con't.)
   Intro to quadradic programming
   Intro to concepts of primal/dual
   SVM dual representation
   Nonlinear SVMs
   Kernels and the Kernel Trick
Handout: pages 102-109 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.

Thurs. 11/08 SVMs (con't.)
   Soft margin classifiers
   Multi-Class Classification
Handout: pages 185-202, 114-122 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.

Tues. 11/13 AdaBoosting and connection to SVMs
AdaBoosting with SVMs for unbalanced data Handout: A Short Introduction to Boosting, by Freund and Schapire
Handout: AdaBoost with SVM-based component classifiers, by Li, Wang, and Sung
Handout: AdaBoost.M1 description, by Freund and Schapire

Thurs. 11/15 SVMs (con't.)
   Parameter selection using cross-validation and grid search
Introduction to LIBSVM
Discussion of Project #4 LIBSVM website
Handout: A practical guide to support vector classification, by Hsu, et al.
Project #4 Assigned

Tues. 11/20 Combining learners -- e.g, bagging
Stable/unstable learners
Inductive bias
No Free Lunch Theorem Background reading: A simple algorithm for learning stable machines, by Andonova, et al.

Thurs. 11/22 Happy Thanksgiving! (no class)

Tues. 11/27 Learning Theory
  Probably Approximately Correct Learning
  Sample complexity for finite hypothesis space Ch. 7-7.3 of Mitchell

Thurs. 11/29 Learning Theory
  Probably Approximately Correct Learning (con't.)
  Sample complexity for infinite hypothesis space Ch. 7.4 of Mitchell

Tues. 12/04
(Last class; no final exam)
Wrapup Course

Date	Topics	Assigned Readings
Thurs. 8/23	Course Introduction Intro. to Learning Lecture slides
Tues. 8/28	Designing learning systems (No lecture slides, but see Mitchell's Ch. 1 slides for related material, especially slides 11-21.)	Ch. 1
Thurs. 8/30	Neural Networks Introduction Perceptrons Gradient descent (No lecture slides, but see Mitchell's Ch. 4 slides for related material.)	Ch. 4 of Mitchell "The Basic Ideas in Neural Networks", by Rumelhart et al., Communications of the ACM, 37(3): 87-92, 1994.
Tues. 9/04	Neural Networks (con't.) Stochastic gradient descent Multi-layer feedforward neural networks	Ch. 4 of Mitchell Handout: Notes on Neural Networks
Thurs. 9/06	Neural Networks (con't.) Back Propagation Convergence issues K-fold cross-validation	Ch. 4. of Mitchell
Tues. 9/11	Neural Networks (con't.) Momentum Design and performance issues Exploring ANN design with Sharky Discussion of project #1	Ch. 4 of Mitchell Project 1 assigned
Thurs. 9/13	Neural Networks (con't.) Case studies Evaluation of learning algorithms: Sensitivity, specificity Type I and Type II errors Confusion matrix ROC curve	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. Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets, by Gorman and Sejnowski, Neural Networks, Vol. 1, pgs. 75-89, 1988. 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. 9/18	Tutorial on LaTex Genetic Algorithms Introduction 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
Thurs. 9/20	Genetic Algorithms Representation Crossover operators Selection/Fitness TSP example	Ch. 9.2-9.3 of Mitchell
Tues. 9/25	Genetic Programming Introduction Example: Boolean 11-Multiplexer Generating initial population Dealing with "code bloat"	Ch. 9.4-9.5 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. 9/27	Genetic Programming Closure and Sufficiency Example: Blocks world Example: Santa Fe Ant	Koza handouts from last class
Tues. 10/02	Genetic Programming Example: Symbolic regression Revisiting GOLEM example Discussion of Project #2	Automatic design and manufacture of robotic lifeforms, Nature, vol. 406, August 31, 2000, pg. 974-978. Project #2 Assigned
Thurs. 10/04	Introduction to Reinforcement Learning Markov Property Markov Decision Processes	Ch. 13.1 - 13.2 of Mitchell Ch. 1 of Sutton and Barto text on RL
Tues. 10/09	Guest lecture by TA; instructor on research travel Reinforcement learning Examples: Maze, K-Armed Bandit, etc. Infinite horizon, finite horizon V and Q functions	Ch. 13.3 of Mitchell Ch. 3, 6.5 of Sutton and Barto text on RL
Thurs. 10/11	Fall break, no class
Tues. 10/16	Review of Project #1 Reinforcement learning Q-Learning Non-deterministic Q-learning Conditions for convergence Epsilon greedy action selection	Ch. 13.4 - 13.5 of Mitchell Ch. 6 and 7 of Sutton and Barto text on RL
Thurs. 10/18	Reinforcement learning TD Learning Sarsa (lambda) Eligibility traces Tutorial on Player/Stage Discussion of Mini-Assignment #1 Discussion of Project #3	Paper introducing Player Player/Stage Getting Started Guide (UTK-specific) Player/Stage Documentation (Public-domain website) Mini-Assignment #1 Assigned (easy!!) Project #3 assigned
Tues. 10/23	Guest lecture by TA; instructor on research travel Reinforcement learning (con't.) Case Studies: TD-Gammon Checkers Elevator Dispatch	Ch. 11 of Sutton and Barto text on RL
Thurs. 10/25	UTK Engineer's Day (undergrad engineering students are dismissed if you need to be involved in Engr. Day activities) Exercise overseen by TA; instructor on research travel In-class ML design exercise (non-credit)
Tues. 10/30	Introduction to Support Vector Machines (SVMs)	Handout: pages 744-748 of Artificial Intelligence, 3rd edition, by Russell and Norvig, Prentice-Hall, 2010.
Thurs. 11/01	SVMs (con't.) Maximizing margin formulation Intro to convex optimization	Handout: pages 73-87 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.
Tues. 11/06	SVMs (con't.) Intro to quadradic programming Intro to concepts of primal/dual SVM dual representation Nonlinear SVMs Kernels and the Kernel Trick	Handout: pages 102-109 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.
Thurs. 11/08	SVMs (con't.) Soft margin classifiers Multi-Class Classification	Handout: pages 185-202, 114-122 of Knowledge Discovery with Support Vector Machines, by Lutz Hamel, John Wiley and Sons, Inc., 2009.
Tues. 11/13	AdaBoosting and connection to SVMs AdaBoosting with SVMs for unbalanced data	Handout: A Short Introduction to Boosting, by Freund and Schapire Handout: AdaBoost with SVM-based component classifiers, by Li, Wang, and Sung Handout: AdaBoost.M1 description, by Freund and Schapire
Thurs. 11/15	SVMs (con't.) Parameter selection using cross-validation and grid search Introduction to LIBSVM Discussion of Project #4	LIBSVM website Handout: A practical guide to support vector classification, by Hsu, et al. Project #4 Assigned
Tues. 11/20	Combining learners -- e.g, bagging Stable/unstable learners Inductive bias No Free Lunch Theorem	Background reading: A simple algorithm for learning stable machines, by Andonova, et al.
Thurs. 11/22	Happy Thanksgiving! (no class)
Tues. 11/27	Learning Theory Probably Approximately Correct Learning Sample complexity for finite hypothesis space	Ch. 7-7.3 of Mitchell
Thurs. 11/29	Learning Theory Probably Approximately Correct Learning (con't.) Sample complexity for infinite hypothesis space	Ch. 7.4 of Mitchell
Tues. 12/04	(Last class; no final exam) Wrapup Course

CS425/528: Machine Learning

Schedule/Readings/Notes Fall 2012

Schedule/Readings/Notes
Fall 2012