In order for you to get the appropriate errata list, you must first determine which printing of the text you have. Go to the page in the text after the title page and before the dedication page where you will find the Library of Congress Cataloging-in-Publication Data and other information. At the bottom, you will see:

Printed in the United States of America

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The last number tells you the printing. Thus in the case here, the 5 indicates the fifth printing.

• Printings 1 and 2
• Between the second and third printings, new theoretical results were announced that warranted inclusion in the text (simple methods for making inferences in Bayes belief nets that contained loops). As such, all printings after the second have a revised section 2.11, Bayes belief nets. If you have printing 1 or 2 and would like a postscript version of this revised section click here.
• Printings 3 and 4
• Printing 5
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Thanks in great part to Djamel Bouchaffra and Vittorio Murino there is now a set of Powerpoint slides to accompany the text. If you have Powerpoint slides that you'd like to contribute, please contact David Stork.

• Chapter 1
• Chapter 2, part 1
• Chapter 2, part 2
• Chapter 2, part 3
• Chapter 3, part 1
• Chapter 3, part 2
• Chapter 3, part 3
• Chapter 4, part 1
• Chapter 4, part 2
• Chapter 5
• Chapter 6
• Chapter 7
• Chapter 8
• Chapter 9
• Chapter 10
• Appendix

• Preface
• Chapter 1: Introduction
  • 1.1 Machine perception
  • 1.2 An example
    • 1.2.1 Related fields
  • 1.3 Pattern recognition systems
    • 1.3.1 Sensing
    • 1.3.2 Segmentation and grouping
    • 1.3.3 Feature extraction
    • 1.3.4 Classification
    • 1.3.5 Post processing
  • 1.4 The design cycle
    • 1.4.1 Data collection
    • 1.4.2 Feature choice
    • 1.4.3 Model choice
    • 1.4.4 training
    • 1.4.5 Evaluation
    • 1.4.6 Computational complexity
  • 1.5 Learning and adaptation
    • 1.5.1 Supervised learning
    • 1.5.2 Unsupervised learning
    • 1.5.3 Reinforcement learning
  • 1.6 Conclusion
  • Summary by chapters
  • Bibliographical and historical remarks
  • Bibliography
• Chapter 2: Bayesian decision theory
  • 2.1 Introduction
  • 2.2 Bayesian decision theory–Continuous features
    • 2.2.1 Two-category classification
  • 2.3 Minimum-error-rate classification
    • *2.3.1 Minimax criterion
    • 2.3.2 Neyman-Pearson criterion
  • 2.4 Classifiers, discriminant functions, and decision surfaces
    • 2.4.1 The multicategory case
    • 2.4.2 The two-category case
  • 2.5 The normal density
    • 2.5.1 Univariate density
    • 2.5.2 Multivariate density
  • 2.6 Discriminant function for the normal density
    • 2.6.1 Case 1: Σi = σ2 I
    • 2.6.2 Case 2: Σi = Σ
    • 2.6.2 Case 3: Σi = arbitrary
  • *2.7 Error probabilities and integrals
  • *2.8 Error bounds for normal densities
    • 2.8.1 Chernoff bound
    • 2.8.2 Bhattacharyya bound
    • 2.8.3 Signal detection theory and operating characteristics
  • 2.9 Bayes decision theory—discrete features
    • 2.9.1 Independent binary features
  • *2.10 Missing and noisy features
    • 2.10.1 Missing features
    • 2.10.2 Noisy features
  • *2.11 Bayesian belief networks
  • *2.12 Compound Bayesian decision theory and context
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 3: Maximum-likelihood and Bayesian parameter estimation
  • 3.1 Introduction
  • 3.2 Maximum-likelihood estimation
    • 3.2.1 The general principle
    • 3.2.2 The Gaussian case: Unknown μ
    • 3.2.3 The Gaussian case: Unknown μ and Σ
    • 3.2.4 Bias
  • 3.3 Bayesian estimation
    • 3.3.1 The class-conditional densities
    • 3.3.2 The parameter distribution
  • 3.4 Bayesian parameter estimation: Gaussian case
    • 3.4.1 The univariate case: p(μ|D)
    • 3.4.2 The univariate case: p(x|D)
    • 3.4.3 The multivariate case
  • 3.5 Bayesian parameter estimation: General theory
    • 3.5.1 When do maximum-likelihood and Bayes methods differ?
    • 3.5.2 Noninformative priors and invariance
    • 3.5.3 Gibbs algorithm
  • *3.6 Sufficient statistics
    • 3.6.1 Sufficient statistics and the exponential family
  • 3.7 Problems of dimensionality
    • 3.7.1 Accuracy, dimension, ad training sample size
    • 3.7.2 Computational complexity
    • 3.7.3 Overfitting
  • *3.8 Component analysis and discriminants
    • 3.8.1 Principal component analysis (PCA)
    • 3.8.2 Fisher linear discriminant
    • 3.8.3 Multiple discriminant analysis
  • *3.9 Expectation-maximization (EM)
  • 3.10 Hidden Markov models
    • 3.10.1 First-order Markov models
    • 3.10.2 First-order hidden Markov models
    • 3.10.3 Hidden Markov model computation
    • 3.10.4 Evaluation
    • 3.10.5 Decoding
    • 3.10.6 Learning
  • Summary
  • Biographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 4: Nonparametric techniques
  • 4.1 Introduction
  • 4.2 Density estimation
  • 4.3 Parzen windows
    • 4.3.1 Convergence of the mean
    • 4.3.2 Convergence of the variance
    • 4.3.3 Illustrations
    • 4.3.4 Classification problem
    • 4.3.5 Probabilistic neuralnetworks (PNNs)
    • 4.3.6 Choosing the window function
  • 4.4 kn-nearest-neighbor estimation
    • 4.4.1 kn-nearest-neighbor and Parzen-window estimation
    • 4.4.2 Estimation of a priori probabilities
  • 4.5 The nearest-neighbor rule
    • 4.5.1 Convergence of the nearest neighbor
    • 4.5.2 Error rate for the nearest-neighbor rule
    • 4.5.3 Error bounds
    • 4.5.4 the k-nearest-neighbor rule
    • 4.5.5 Computational complexity of the k-nearest-neighbor rule
  • 4.6 Metrics and nearest-neighbor classification
    • 4.6.1 Properties of metrics
    • 4.6.2 Tangent distance
  • *4.7 Fuzzy classification
  • *4.8 Reduced Coulomb energy networks
  • 4.9 Approximations by series expansions
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 5: Linear discriminant functions
  • 5.1 Introduction
  • 5.2 Linear discriminant functions and decision surfaces
    • 5.2.1 The two-category case
    • 5.2.2 The multicategory case
  • 5.3 Generalized linear discriminant functions
  • 5.4 The two-category linearly separable case
    • 5.4.1 Geometry and terminology
    • 5.4.2 Gradient descent procedures
  • 5.5 Minimizing the perceptron criterion function
    • 5.5.1 The perceptron criterion function
    • 5.5.2 Convergence proof for single-sample correction
    • 5.5.3 Some direct generalizations
  • 5.6 Relaxation procedures
    • 5.6.1 The descent algorithm
    • 5.6.2 Convergence proof
  • 5.7 Nonseparable behavior
  • 5.8 Minimum squared-error procedures
    • 5.8.1 Minimumsquared-error and the pseudoinverse
    • 5.8.2 Relation to Fisher's linear discriminant
    • 5.8.3 Asymptotic approximation to an optimal discriminant
    • 5.8.4 The Widrow-Hoff or LMS procedure
    • 5.8.5 Stochastic approximation methods
  • 5.9 The Ho-Kashyap procedures
    • 5.9.1 The descent procedure
    • 5.9.2 Convergence proof
    • 5.9.3 Nonseparable bhaviorUntitled Item
    • 5.9.4 Some related procedures
  • *5.10 Linear programming algorithms
    • 5.10.1 Linear programming
    • 5.10.2 The linearly separable case
    • 5.10.3 Minimizing the perceptron criterion function
  • *5.11 Support vector machines
    • 5.11.1 SVM training
  • 5.12 Multicategory generalizations
    • 5.12.1 Kesler's construction
    • 5.12.2 Convergence of the fixed-increment rule
    • 5.12.3 Generalizations for MSE procedures
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 6: Multilayer neural networks
  • 6.1 Introduction
  • 6.2 Feedforward operation and classification
    • 6.2.1 General feedforward operation
    • 6.2.2 Expressive power of multilayer networks
  • 6.3 Backpropagation algorithm
    • 6.3.1 Network learning
    • 6.3.2 Training protocols
    • 6.3.3 Learning curves
  • 6.4 Error surfaces
    • 6.4.1 Some small networks
    • 6.4.2 The exclusive-OR (XOR)
    • 6.4.3 Larger networks
    • 6.4.4 How important are multiple minima?
  • 6.5 Backpropagation as feature mapping
    • 6.5.1 Representations at the hidden layer—weights
  • 6.6 Backpropagation, Bayes theory and probability
    • 6.6.1 Bayes discriminants and neural networks
    • 6.6.2 Outputs as probabilities
  • *6.7 Related statistical techniques
  • 6.8 Practical techniques for improving backpropagation
    • 6.8.1 Activation function
    • 6.8.2 Parameters for the sigmoid
    • 6.8.3 Scaling input
    • 6.8.4 Target values
    • 6.8.5 Training with noise
    • 6.8.6 Manufacturing data
    • 6.8.9 Learning rates
    • 6.8.10 Momentum
    • 6.8.11 Weight decay
    • 6.8.12 Hints
    • 6.8.13 On-line, stochastic or batch training?
    • 6.8.14 Stopped training
    • 6.8.15 Number of hidden layers
    • 6.8.16 Criterion function
  • *6.9 Second-order methods
    • 6.9.1 Hessian matrix
    • 6.9.2 Newton's method
    • 6.9.3 Quickprop
    • 6.9.4 Conjugate gradient descent
  • *6.10 Additional networks and training methods
    • 6.10.1 Radial basis function networks (RBFs)
    • 6.10.2 Special bases
    • 6.10.3 Matched filters
    • 6.10.4 Convolutional networks
    • 6.10.5 Recurrent networks
    • 6.10.6 Cascade-correlation
  • 6.11 Regularization, complexity adjustment and pruning
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 7: Stochastic methods
  • 7.1 Introduction
  • 7.2 Stochastic search
    • 7.2.1 Simulated annealing
    • 7.2.2 The Boltzmann factor
    • 7.2.3 Deterministic simulated anealing
  • 7.3 Boltzmann learning
    • 7.3.1 Stochastic Boltmann learning of visible states
    • 7.3.2 Missing features and category constraints
    • 7.3.3 Deterministic Boltzmann learning
    • 7.3.4 Initialization and setting parameters
  • *7.4 Boltzmann networks and graphical models
    • 7.4.1 Other graphical models
  • *7.5 Evolutionary methods
    • 7.5.1 Genetic algorithms
    • 7.5.2 Further heuristics
    • 7.5.3 Why do they work?
  • *7.6 Genetic programming
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 8: Nonmetric methods
  • 8.1 Introduction
  • 8.2 Decision trees
  • 8.3 CART
    • 8.3.1 Number of splits
    • 8.3.2 Query selection and node impurity
    • 8.3.3 When to stop splitting
    • 8.3.4 Pruning
    • 8.3.5 Assignment of leaf node labels
    • 8.3.6 Computational complexity
    • 8.3.7 Feature choice
    • 8.3.8 Multivariate decision trees
    • 8.3.9 Priors and costs
    • 8.3.10 Missing attributes
  • 8.4 Other tree methods
    • 8.4.1 ID3
    • 8.4.2 C4.5
    • 8.4.3 Which tree classifier is best?
  • *8.5 Recognition with strings
    • 8.5.1 String matching
    • 8.5.2 Edit distance
    • 8.5.3 Computational complexity
    • 8.5.4 String matching with errors
    • 8.5.5 String matching with the "don't care" symbol
  • 8.6 Grammatical methods
    • 8.6.1 Grammars
    • 8.6.2 Types of string grammars
    • 8.6.3 Recognition using grammars
  • 8.7 Grammatical inference
  • *8.8 Rule-based methods
    • 8.8.1 Learning rules
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 9: Algorithm-independent machine learning
  • 9.1 Introduction
  • 9.2 Lack of inherent superiority of any classifier
    • 9.2.1 No free lunch theorm
    • *9.2.2 Ugly duckling theorem
    • 9.2.3 Minimum description length (MDL)
    • 9.2.4 Minimum description length principle
    • 9.2.5 Overfitting avoidance and Occam's razor
  • 9.3 Bias and variance
    • 9.3.1 Bias and variance for regression
    • 9.3.2 Bias and variance for classification
  • 9.4 Resampling for estimating statistics
    • 9.4.1 Jackknife
    • 9.4.2 Bootstrap
  • 9.5 Resampling for classifier design
    • 9.5.1 Bagging
    • 9.5.2 Boosting
    • 9.5.3 Learning with queries
    • 9.5.4 Arcing, learning with queries, bias and variance
  • 9.6 Estimating and comparing clasifiers
    • 9.6.1 Parametric models
    • 9.6.2 Cross-validation
    • 9.6.3 Jackknife and bootstrap estimation of classification accuracy
    • 9.6.4 Maximum-likelihood model comparison
    • 9.6.5 Bayesian model comparison
    • 9.6.6 The problem-average error rate
    • 9.6.7 Predicting final performance from learning curves
    • 9.6.8 The capacity of a separating plane
  • 9.7 Combining classifiers
    • 9.7.1 Component classifiers with discriminant functions
    • 9.7.2 Component classifiers without discriminant functions
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Chapter 10: Unsupervised learning and clustering
  • 10.1 Introduction
  • 10.2 Mixture densities and identifiability
  • 10.3 Maximum-likelihood estimates
  • 10.4 Application to normal densities
    • 10.4.1 Case 1: Unknown mean vectors
    • 10.4.2: Case 2: All parameters unknown
    • 10.4.3: k-means clustering
    • *10.4.4: Fuzzy k-means clustering
  • 10.5: Unsupervised Bayesian learning
    • 10.5.1: The Bayes classifier
    • 10.5.2: Learning the parameter vector
    • 10.5.3: Decision-directed approximation
  • 10.6 Data description and clustering
    • 10.6.1: Similarity measures
  • 10.7: Criterion functions for clustering
    • 10.7.1 The sum-of-square-error criterion
    • 10.7.2: Related minimum variance criterion
    • 10.7.3: Scatter criteria
  • *10.8: Iterative optimization
  • 10.9: Hierarchical clustering
    • 10.9.1: Definitions
    • 10.9.2: Agglomerative hierarchical clustering
    • 10.9.3: Stepwise-optimal hierarchical clustering
    • 10.9.4: Hierarchical clustering and induced metrics
  • *10.10: The problem of validity
  • *10.11: On-line clustering
    • 10.11.1: Unknown number of clusters
    • 10.11.2: Adaptive resonance
    • 10.11.3: Learning with a critic
  • *10.12: Graph-theoretic methods
  • 10.13: Component analysis
    • 10.13.1: Principal component analysis (PCA)
    • 10.13.2: Nonlinear component analysis (NLCA)
    • *10.13.3: Independent component analysis (ICA)
  • 10.14: Low-dimensional representtions and multidimensional scaling (MDS)
    • 10.14.1: Self-organizing feature maps
    • 10.14.2: Clustering and dimensionality reduction
  • Summary
  • Bibliographical and historical remarks
  • Problems
  • Computer exercises
  • Bibliography
• Appendix: Mathematical foundations
  • A.1: Notation
  • A.2: Linear algebra
    • A.2.1: Notation and preliminaries
    • A.2.2: Inner product
    • A.2.3: Outer product
    • A.2.4: Derivatives of matrices
    • A.2.5: Determinant and trace
    • A.2.6: Matrix inversion
    • A.2.7: Eigenvectors and eigenvalues
  • A.3: Lagrange optimization
  • A.4: Probability theory
    • A.4.1: Discrete random variables
    • A.4.2: Expected values
    • A.4.3: Pairs of discrete random variables
    • A.4.4: Statistical independence
    • A.4.5: Expected values of functions of two variables
    • A.4.6: Conditional probability
    • A.4.7: The law of total probability and Bayes rule
    • A.4.8: Vector random variables
    • A.4.9: Expectations, mean vectors and covariance matrices
    • A.4.10: Continuous random variables
    • A.4.11: Distributions of sums of independent random variables
    • A.4.12: Normal distributions
  • A.5: Gaussian derivatives and integrals
    • A.5.1: Multivariate normal densities
    • A.5.2: Bivariate normal densities
  • A.6: Hypothesis testing
    • A.6.1: Chi-squared test
  • A.7: Information theory
    • A.7.1 Entropy and informationUntitled Item
    • A.7.2: Relative entropy
    • A.7.3: Mutual information
  • Computational complexity
• Index

The Computer Manual in MATLAB to accompany Pattern Classification (2nd ed.) by David G. Stork and Elad Yom-Tov is now available. You must purchase the Manual first, which gives passwords and download information for the current version of the MATLAB software. The software is no longer available for free.

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Institution	City	State	Course site
Air Force Institute of Technology	Akron	OH	EENG 620
Arizona State University	Phoenix	AZ	CSE591
Boston University	Boston	MA	CN520, CN550
Brighan Young University - Provo	Provo	UT	CS621
Brown University	Providence	RI	ENG252, CS295
California Institute of Technology	Pasadena	CA	CSci5980-64841
Carnegie-Mellon University	Pittsburgh	PA	ECE794, 10-701/15-781
City College of New York	New York	NY	I1900
Colorado School of Mines	Golden	CO	MACS 598D
Colorado State University	Fort Collins	CO	M676
Columbia University	New York	NY	ELENE6880
Cornell University	Ithaca	NY	CS478
Depaul University	Chicago	IL	CSC578
Farleigh Dickenson University	Teaneck	NJ	CSCI7775
Florida A & M University	Tallahassee	FL	EE-5930
Florida International University	Miami	FL	EEL6825
George Mason University	Fairfax	VA	IT844
Georgetown University	Washington	DC	BIST531
Georgia Institute of Technology	Atlanta	GA	CS4803
Harvard University	Cambridge	MA	CS283, HST951
Harvey Mudd College	Claremont	CA	ENG161, MLSeminar
Illinois Institute of Technology	Chicago	IL	ECE566
Iowa State University	Ames	IA	CS573, EE547, CS474
Kansas State University	Manhattan	KS	CIS830
Lamar University	Beaumont	TX	CPSC5370
Lehigh University	Bethlehem	PA	CSE397/497
Marquette University	Milwaukee	WI	EE212
Massachusetts Institute of Technology	Cambridge	MA	MAS622J
Medical University of South Carolina	Charleston	SC	BMTRY789-04,Geog763
Michigan State University	East Lansing	MI	CSE802
Mississippi State	Mississippi State	MS	ECE8443, CS9633
Montana State University	Bozeman	MO	CS530
New Jersey Institute of Technology	Newark	NJ	CS782
New York University	New York	NY	G22-2565-001, G22-3033-014
Northeastern University	Boston	MA	CSU520
Northwestern University	Evanston	IL	ECE510
Oakland University	Rochester	MI	CSE616
Ohio State University	Columbus	OH	CIS694
Old Dominion University	Norfolk	VA	ECE781/881
Oregon Graduate Institute	Beaverton	OR	CSE547
Oregon State University	Corvallis	OR	CS534
Pace University	New York	NY	CS396
Pennsylvania State University	University Park	PA	CSE/EE583/586
Princeton University	Princeton	NJ	ELE535, CS511
Rensselaer Polytechnic Institute	Troy	NY	ECSE35-6610
Rochester Institute of Technology	Rochester	NY	4005-755, CE785
Rowan University	Glassboro	NJ	ECE504
Rutgers University	Newark	NJ	DCS520
Santa Clara University	Santa Clara	CA	COEN 296
Southern Illinois University - Carbondale	Carbondale	IL	ECE568
Southern Methodist University	Dallas	TX	EE8364
State University of New York at Oneonta	Oneonta	NY	CSCI324
Stanford University	Stanford	CA	EE292D, CS229, Stat252
State University of New York at Stony Brook	Stony Brook	NY	CSE615
Swarthmore College	Swarthmore	PA	CS97
Temple University	Philadelphia	PA	CIS595-2, CIS526
Texas A&M University - College Station	College Station	TX	ELEN649
Texas Tech University	Lubbock	TX	CS5341, CS6363
Tufts University	Medford	MA	Comp150ML
Tulane University	New Orleans	LA	CPSC469
University of Bridgeport	Bridgeport	CT	EE453X
University of Buffalo	Buffalo	NY	CSE655
University of California - Berkeley	Berkeley	CA	CS365, EECS225d
University of California - Davis	Davis	CA	EEC207
University of California - Irvine	Irvine	CA	ICS274, ICS178
University of California - Los Angeles	Los Angeles	CA	STAT231,CS273A
University of California - Riverside	Riverside	CA	EE240
University of California - San Diego	San Diego	CA	CSE291, CSE253, Cogsci260
University of California - Santa Barbara	Santa Barbara	CA	CS/ECE281B, CS290I
University of Florida	Gainesville	FL	EEL6825
University of Hawaii	Honolulu	HI	ICS663, ICS483
University of Houston	Houston	TX	COSC6342
University of Illinois	Chicago	IL	ECE407
University of Illinois	Urbana	IL	CS346
University of Iowa	Iowa City	IA	55:145
University of Louisville	Louisville	KT	ECE620
University of Maryland - Baltimore County	Baltimore	MD	CMSC 491L/691L
University of Maryland - College Park	College Park	MD	EENE739Q/CMSC858C, ENEE633
University of Massachusetts - Amherst	Amherst	MA	CS689
University of Memphis	Memphis	TN	EECE7902/8902
University of Michigan	Ann Arbor	MI	IOE591
University of Minnesota - Twin Cities	Minneapolis	MN	CS5521
University of Mississippi	University	MS	ENGR691/692
University of Missouri - Kansas City	Kansas City	MO	ECE486
University of New Mexico	Albuquerque	NM	ECE517
University of New Orleans	New Orleans	LA	CSCI6635
University of Nevada	Reno	NV	CS679
University of New Orleans	New Orleans	LA	CSCI6990
University of North Carolina - Charlotte	Charlotte	NC	COMP254
University of North Carolina - Wilmington	Wilmington	NC	CS577
University of Omaha	Omaha	NB	CSCI4470
University of Pennsylvania	Philadelphia	PA	CIS700-002
University of Pittsburgh	Pittsburgh	PA	EE2372/CS3790, CS2750
University of South Alabama	Mobile	AL	EE590
University of South Carolina	Columbia	SC	CSCE768, GEOG763
University of Southern California	Los Angeles	CA	CS567, EE559, CSCI567
University of South Florida	Tampa	FL	CAP6638
University of Tennessee	Knoxville	TN	ECE471
University of Texas - Arlington	Arlington	TX	CSE5392
University of Texas - Austin	Austin	TX	EE380L, CS395T
University of Vermont	Burlington	VT	CS295, CS355
University of Washington	Seattle	WA	EE596,STAT/MATH390B
University of Wisconsin - Madison	Madison	WI	ECE532
University of Wisconsin - Milwaukee	Milwaukee	WI	EE/CS711
University of Wyoming	Laramie	WY	EE5880-04
Utah State University	Logan	UT	CS5650
Villanova University	Philadelphia	PA	CS 4803B/8803B
Virginia Commonwealth University	Richmond	VA	ENGR691
Virginia Tech University	Blacksburg	VA	ECE5524
Wake Forest University	Winston-Salem	NC	ME725, CS766
Walden University	Gainesville	FL	EEL6825
Wayne State Universit	Detroit	MI	PatRec
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Wright State University	Dayton	OH	CEG799
Non-US universities	City	Country	Course Site
Aalborg University	Aalborg	Denmark	MachLearn
Ain Shams University	Abbassia	Egypt	CSC446
Al-Balqa Applied University	Salt	Jordan	301001466
American University of Beirut	Beirut	Lebanon	xxxxx
Andalou University	Andalou	Turkey	xxxxx
Bahcesehir Univeristy	Istambul	Turkey	CMPE5550
Beijing Jiaotong University	Beijing	China	xxxxx
Bilkent University	Ankara	Turkey	CS551
B. M. Srinivasaiah College of Engineering	Bangalore	India	CS755
Bogaziçi University	Istambul	Turkey	CmpE544, EE680
Cambridge University	Cambridge	United Kingdom	3F3
Center for Advanced Studies in Engineering	Islamabad	Pakistan	ECE6281
Chinese University of Hong Kong	Hong Kong	Hong Kong	xxxx
Chonbuk National University	Chonbuk	S. Korea	StatDataBase
Chulalongkorn University	Bankok	Thailand	2110495
Chung Hua University	Hsinchu	Taiwan	PR
Chung-Yuan Christian University	Chung-Li	Taiwan	EL507R
Concordia University	Montréal	Canada	COMP473
Cultural and Educational Technology Institute	Xanthi	Greece	PatClas
Dokuz Eylül University	Buca, Izmir	Turkey	EEE573
Dongguk University	Seoul	S. Korea	xxxxx
Eins-Shams University	Cairo	Egypt	xxxx
ESME Sudria	Ivry sur Seine	France	SigProc
Escola Superior de Tecnologia	Setúbal	Portugal	RecdePad
Electric Power Research Institute of China	Beijing	China	xxxxx
FAST-NUCES	Lahore	Pakistan	PatRec
Freie Universität Berlin	Berlin	Germany	19520V
Fudan University	Shanghai	China	PatRec
Gebze Institute of Technology	Kocaeli	Turkey	xxxxx
Guru Gobind Singh Indraprastha University	Delhi	India	IT514
Harbin Institute of Technology	Harbin	China	xxxx
Helwan University	Cairo	Egypt	IT 342
Hong Kong University of Science and Technology	Hong Kong	Hong Kong	COMP522
Hosei University	Ichigaya	Japan	PatRec
Indian Institute of Technology (IIT) - Bangalore	Bangalore	India	E1-213
Indian Institute of Technology (IIT) - Bombay	Bombay	India	xxxxx
Indian Institute of Technology (IIT) - Madras	Madras	India	CS699, CS635
India Institute of Technology (IIT) - Kanpur	Kanpur	India	CS674
Information and Communication University	Taejon	S. Korea	ICE631
India Institute of Science	Bangalore	India	E1 213
Instituto de Telecomunicações	Lisboa	Portugal	LearningTheory
Islamic University	Gaza	Palestine	EELE6337
Istanbul Teknik Üniversitesi	Istambul	Turkey	BLG601, BBL514E
International Institute of Information Technology	Hyderabad	India	CS4770
Izmir Institute of Technology	Urla	Turkey	EE 546
Joensuu University	Joensuu	Finland	173341
Johannes Kepler University	Linz	Austria	344.009
Koç University	Istambul	Turkey	ECOE554(1)
Korea University of Technology and Education	Pyungchon	S. Korea	xxxxx
Kumamoto University	Kumamoto	Japan	02650
Kungl Tekniska Högskolan	Stockholm	Sweden	2F1510
Kwangju Institute of Science and Technology	Kwangju	Korea	DIC1683
KyungPook University	Daegu	S. Korea	ELEC801-01
Lahore University of Management Science	Lahore	Pakistan	CS533
Lappeenranta University of Technology	Lappeenranta	Finland	010586001
McGill University	Montréal	Canada	CS644, COMP652
McMaster University	Hamilton	Canada	ECE766
Memorial University of Newfoundland	St. John's	CA	ENG8801/9881
Mersin University	Mersin	Turkey	BIO364
Middle East Technical University	Ankara	Turkey	EE583, CENG564
Monash University	Gippsland	Australia	CSE3320
Monterrey Technical University	Monterrey	Mexico	IA-95-115
Multimedia University	Cyberjaya Selangor	Malaysia	TPR7411
Nagaoka University	Nagaoka	Japan	AdvPatRec
Nanyang Technological University	Singapore	Singapore	H6429
National Chung Hsing University	Taichung	Taiwan	6685
National Taiwan Normal University	Taipei	Taiwan	PatRec
National Taiwan University	Taipei	Taiwan	526U0350
National Tsing Hua University	HsingChu	Taiwan	CS5540
National University of Singapore	Singapore	Singapore	ME4247, SMA5514
Northeast Normal University	Changchun	China	1273013
Northwestern Polytechnical University	Xian	China	106017
Osmania University	Hyderabad	India	EC578
PES School of Engineering	Bangalore	India	PatternRecognition
Pusan National University	Busan	S. Korea	ECE/PR
Queen's University	Kingston	Canada	CISC859
Queen Mary, University of London	London	England	Intelligent Systems
Rijksuniversiteit Groningen	Groningen	The Netherlands	ScriptRec, INPR03
Royal Institute of Technology	Stockholm	Sweden	2E1395
Royal University of Dhaka City	Dhaka	Bangladesh	CSIT331/CSIT332
Ryerson University	Toronto	Canada	IntSys
Saarland University	Saarbrücken	Germany	Pat&SpeechRec
Sabanci University	Istambul	Turkey	EE566
Shandong University	Shandong	China	xxxxx
Sharif University of Technology	Tehran	Iran	40-725
South-Central University for Nationalities	Wuhan	China	PatRec
Southwestern University of Finance and Economics	ChengDu	China	2060620080
Technical University of Delft	Delft	The Netherlands	AdvPatRec
Technical University of Isfahan	Isfahan	Iran	xxxxx
Technical University of Sophia	Sophia	Bulgaria	MAICE04, MCCT14b
Technion	Haifa	Israel	EE48995
Technische Universität Graz	Graz	Austria	WS02
Tampere University of Technology	Tampere	Finland	SGN-2506
Thammasat University	Bangkok	Thailand	CN335, LE645
Toho University	Tokyo	Japan	xxxxx
Tsinghua University	Beijing	China	80240023
Tsukuba University	Tsukuba Science City	Japan	CF013
Universida Nacional de La Plata	La Plata	Argentina	R49,C72
Universidad del Zulia	Maracaibo	Venezuela	2120360001180
Universidad Politécnica de Madrid	Madrid	Spain	9810
Universidad de Concepción	Casilla	Chile	AI
Universidad de Murcia	Murcia	Spain	SysPerc
Universidade de Brasília	Brasília	Brazil	6.1.1.13
Universidade de São Paulo	São Paulo	Brazil	MAC432, EAE5882
Universidade Federal do Rio de Janeiro	Rio de Janeiro	Brazil	xxxxx
Università di Roma "La Sapienza"	Rome	Italy	Apprendimento
Università di Verona	Verona	Italy	PatternRecognition
Universitas Miguel Hernàndez	Elche	Spain	IARP
Universitat Politécnica de Catalunya	Barcelona	Spain	31050, 11539
Universität Bremen	Bremen	Germany	MustKlass
Universität Dortmund	Dortmund	Germany	042523, 042637
Universität Karlsruhe	Karlsruhe	Germany	RecSys
Università degli Studi di Cassino	Cassino	Italy	ImageInterpretation
Università degli studi di Trento	Trento	Italy	361
Università di Parma	Parma	Italy	xxxxx
Universitat Politècnica de Valencia	València	Spain	RedesNeur
Universitatea Tehnica "Gh. Asachi" IASI	Iasi	Romania	HCI
Université d'Avignon	Avignon	France	PatternRecognition
Université de Bourgogne	Bourgogne	France	PatRec
Université de Genève	Geneva	Switzerland	DMCours
Université de Montréal	Montréal	Canada	IFT6141
Université du Québec a Montréal	Montréal	Canada	INF7740
Université Laval	Quebec	Canada	IFT64321
Universiteit Maastricht	Maastricht	The Netherlands	M.1ML
University College London	London	United Kingdom	GI01/4C55
University of Alberta	Edmonton	Canada	Cmput466
University of Birmingham	Birmingham	United Kingdom	06-02640
University of Brescia	Brescia	Italy	StatPR
University of Bristol	Bristol	United Kingdom	COMS21202
University of British Columbia	Vancouver	Canada	CPSE505, STAT406
University of Cagliari	Cagliari	Italy	ING-INF05
University of Canberra	Canberra	Australia	xxxxx
University of Crete	Heraklion	Greece	CS473
University of Dundee	Dundee	United Kingdom	AC4261
University of Edinburgh	Edinburgh	Scotland	CogVision
University of Engineering and Technology	Peshawar	Pakistan	ECE248
University of Helsinki	Helskinki	Finland	582457
University of Hiroshima	Hiroshima	Japan	PIP
University of Hyderabad	Hyderabad	India	CS743
University of Iceland	Reykjavik	Iceland	8.33.02
University of Indonesia	Depok	Indonesia	xxxxx
University of Jordan	Amman	Jordan	xxxxx
University of Kaiserlauten	Kaiserlauten	Germany	SS06
University of Liverpool	Liverpool	United Kingdom	COMP316
University of London, Royal Holloway	London	United Kingdom	CS392
University of Manitoba	Winnipeg	Canada	24.840
University of New South Wales	Sydney	Australia	CS9518
University of Puerto Rico	Mayaguez	Puerto Rico	CIIC8015, INEL 5046
University of Queensland	Queensland	Australia	ELEC7605
University of Regina	Regina	Canada	CS835
University of the Aegean	Karlovassi-Samos	Greece	xxxxx
University of Toronto	Toronto	Canada	ECE1529, CSC411
University of Toronto	Mississauga	Canada	CS411
University of Turku	Turku	Finland	TKO5519
University of Vienna	Vienna	Austria	509.707
University of Waterloo	Waterloo	Canada	CS786
University of Westminster	London	United Kingdom	2AIT608
Visvesvaraiah Technological University	Belgaum	India	PatRec
Yonsei University	Seoul	Korea	DB522-01
York University	York	Canada	COSC4422
Zhejiang University	Hangzhou	China	Pattern recognition

If you have further questions, please feel free to contact me David G. Stork. However, please do not ask me to explain material from the book, answer a homework problem or give help on computer exercises.