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Edited by Nong Ye, Lawrence Erlbaum Associates, Inc, 2003 Table of Content I: METHODOLOGIES OF DATA MINING 1 Decision Trees 2 Association Rules 3 Artificial Neural Network Models for Data Mining 4 Statistical Analysis of Normal and Abnormal Data 5 Bayesian Data Analysis 6 Hidden Markov Processes and Sequential Pattern Mining 7 Strategies and Methods for Prediction 8 Principal Components and Factor Analysis 9 Psychometric Methods of Latent Variable Modeling 10 Scalable Clustering 11 Time Series Similarity and Indexing 12 Nonlinear Time Series Analysis 13 Distributed Data Mining II: MANAGEMENT OF DATA MINING 14 Data Collection, Preparation, Quality, and Visualization 15 Data Storage and Management 16 Feature Extraction, Selection, and Construction 17 Performance Analysis and Evaluation 18 Security and Privacy 19 Emerging Standards and Interfaces 20 Mining Human Performance Data 21 Mining Text Data 22 Mining Geospatial Data 23 Mining Science and Engineering Data 24 Mining Data in Bioinformatics 25 Mining Customer Relationship Management (CRM) Data 26 Mining Computer and Network Security Data 27 Mining Image Data 28 Mining Manufacturing Quality Data

I Practical Algorithm Design 1 Introduction to Algorithm Design 1.1 Robot Tour Optimization 1.2 Selecting the Right Jobs 1.3 Reasoning about Correctness 1.4 Modeling the Problem 1.5 About theWar Stories 1.6 War Story: PsychicModeling 1.7 Exercises 2 Algorithm Analysis 2.1 The RAM Model of Computation 2.2 The Big Oh Notation 2.3 Growth Rates and Dominance Relations 2.4 Working with the Big Oh 2.5 Reasoning About Efficiency 2.6 Logarithms and Their Applications 2.7 Properties of Logarithms 2.8 War Story: Mystery of the Pyramids 2.9 Advanced Analysis (*) 2.10 Exercises 3 Data Structures 3.1 Contiguous vs. Linked Data Structures 3.2 Stacks and Queues 3.3 Dictionaries 3.4 Binary Search Trees 3.5 Priority Queues 3.6 War Story: Stripping Triangulations 3.7 Hashing and Strings 3.8 Specialized Data Structures 3.9 War Story: String ’em Up 3.10 Exercises 4 Sorting and Searching 4.1 Applications of Sorting 4.2 Pragmatics of Sorting 4.3 Heapsort: Fast Sorting via Data Structures 4.4 War Story: Give me a Ticket on an Airplane 4.5 Mergesort: Sorting by Divide-and-Conquer 4.6 Quicksort: Sorting by Randomization 4.7 Distribution Sort: Sorting via Bucketing 4.8 War Story: Skiena for the Defense 4.9 Binary Search and Related Algorithms 4.10 Divide-and-Conquer 4.11 Exercises 5 Graph Traversal 5.1 Flavors of Graphs 5.2 Data Structures for Graphs 5.3 War Story: I was a Victim ofMoore’s Law 5.4 War Story: Getting the Graph 5.5 Traversing a Graph 5.6 Breadth-First Search 5.7 Applications of Breadth-First Search 5.8 Depth-First Search 5.9 Applications of Depth-First Search 5.10 Depth-First Search on Directed Graphs 5.11 Exercises 6 Weighted Graph Algorithms 6.1 Minimum Spanning Trees 6.2 War Story: Nothing but Nets 6.3 Shortest Paths 6.4 War Story: Dialing for Documents 6.5 Network Flows and Bipartite Matching 6.6 Design Graphs, Not Algorithms 6.7 Exercises 7 Combinatorial Search and Heuristic Methods 7.1 Backtracking 7.2 Search Pruning 7.3 Sudoku 7.4 War Story: Covering Chessboards 7.5 Heuristic SearchMethods 7.6 War Story: Only it is Not a Radio 7.7 War Story: Annealing Arrays 7.8 Other Heuristic SearchMethods 7.9 Parallel Algorithms 7.10 War Story: Going Nowhere Fast 7.11 Exercises 8 Dynamic Programming 8.1 Caching vs. Computation 8.2 Approximate String Matching 8.3 Longest Increasing Sequence 8.4 War Story: Evolution of the Lobster 8.5 The Partition Problem 8.6 Parsing Context-Free Grammars 8.7 Limitations of Dynamic Programming: TSP 8.8 War Story: What’s Past is Prolog 8.9 War Story: Text Compression for Bar Codes 8.10 Exercises 9 Intractable Problems and Approximation Algorithms 9.1 Problems and Reductions 9.2 Reductions for Algorithms 9.3 Elementary Hardness Reductions 9.4 Satisfiability 9.5 Creative Reductions 9.6 The Art of Proving Hardness 9.7 War Story: Hard Against the Clock 9.8 War Story: And Then I Failed 9.9 P vs. NP 9.10 Dealing with NP-complete Problems 9.11 Exercises 10 How to Design Algorithms II The Hitchhiker’s Guide to Algorithms 11 A Catalog of Algorithmic Problems 12 Data Structures 12.1 Dictionaries 12.2 Priority Queues 12.3 Suffix Trees and Arrays 12.4 Graph Data Structures 12.5 Set Data Structures 12.6 Kd-Trees 13 Numerical Problems 13.1 Solving Linear Equations 13.2 Bandwidth Reduction 13.3 Matrix Multiplication 13.4 Determinants and Permanents 13.5 Constrained and Unconstrained Optimization 13.6 Linear Programming 13.7 Random Number Generation 13.8 Factoring and Primality Testing 13.9 Arbitrary-Precision Arithmetic 13.10 Knapsack Problem 13.11 Discrete Fourier Transform 14 Combinatorial Problems 14.1 Sorting 14.2 Searching 14.3 Median and Selection 14.4 Generating Permutations 14.5 Generating Subsets 14.6 Generating Partitions 14.7 Generating Graphs 14.8 Calendrical Calculations 14.9 Job Scheduling 14.10 Satisfiability 15 Graph Problems: Polynomial-Time 15.1 Connected Components 15.2 Topological Sorting 15.3 Minimum Spanning Tree 15.4 Shortest Path 15.5 Transitive Closure and Reduction 15.6 Matching 15.7 Eulerian Cycle/Chinese Postman 15.8 Edge and Vertex Connectivity 15.9 Network Flow 15.10 Drawing Graphs Nicely 15.11 Drawing Trees 15.12 Planarity Detection and Embedding 16 Graph Problems: Hard Problems 16.1 Clique 16.2 Independent Set 16.3 Vertex Cover 16.4 Traveling Salesman Problem 16.5 Hamiltonian Cycle 16.6 Graph Partition 16.7 Vertex Coloring 16.8 Edge Coloring 16.9 Graph Isomorphism 16.10 Steiner Tree 16.11 Feedback Edge/Vertex Set 17 Computational Geometry 562 17.1 Robust Geometric Primitives 17.2 Convex Hull 17.3 Triangulation 17.4 Voronoi Diagrams 17.5 Nearest Neighbor Search 17.6 Range Search 17.7 Point Location 17.8 Intersection Detection 17.9 Bin Packing 17.10 Medial-Axis Transform 17.11 Polygon Partitioning 17.12 Simplifying Polygons 17.13 Shape Similarity 17.14 Motion Planning 17.15 Maintaining Line Arrangements 17.16 Minkowski Sum 18 Set and String Problems 18.1 Set Cover 18.2 Set Packing 18.3 String Matching 18.4 Approximate String Matching 18.5 Text Compression 18.6 Cryptography 18.7 Finite State Machine Minimization 18.8 Longest Common Substring/Subsequence 18.9 Shortest Common Superstring 19 Algorithmic Resources 19.1 Software Systems 19.2 Data Sources 19.3 Online Bibliographic Resources 19.4 Professional Consulting Services Bibliography Index

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The authoritative textbook for reinforcement learning by Richard Sutton and Andrew Barto. Contents Preface Series Forward Summary of Notation I. The Problem 1. Introduction 1.1 Reinforcement Learning 1.2 Examples 1.3 Elements of Reinforcement Learning 1.4 An Extended Example: Tic-Tac-Toe 1.5 Summary 1.6 History of Reinforcement Learning 1.7 Bibliographical Remarks 2. Evaluative Feedback 2.1 An -Armed Bandit Problem 2.2 Action-Value Methods 2.3 Softmax Action Selection 2.4 Evaluation Versus Instruction 2.5 Incremental Implementation 2.6 Tracking a Nonstationary Problem 2.7 Optimistic Initial Values 2.8 Reinforcement Comparison 2.9 Pursuit Methods 2.10 Associative Search 2.11 Conclusions 2.12 Bibliographical and Historical Remarks 3. The Reinforcement Learning Problem 3.1 The Agent-Environment Interface 3.2 Goals and Rewards 3.3 Returns 3.4 Unified Notation for Episodic and Continuing Tasks 3.5 The Markov Property 3.6 Markov Decision Processes 3.7 Value Functions 3.8 Optimal Value Functions 3.9 Optimality and Approximation 3.10 Summary 3.11 Bibliographical and Historical Remarks II. Elementary Solution Methods 4. Dynamic Programming 4.1 Policy Evaluation 4.2 Policy Improvement 4.3 Policy Iteration 4.4 Value Iteration 4.5 Asynchronous Dynamic Programming 4.6 Generalized Policy Iteration 4.7 Efficiency of Dynamic Programming 4.8 Summary 4.9 Bibliographical and Historical Remarks 5. Monte Carlo Methods 5.1 Monte Carlo Policy Evaluation 5.2 Monte Carlo Estimation of Action Values 5.3 Monte Carlo Control 5.4 On-Policy Monte Carlo Control 5.5 Evaluating One Policy While Following Another 5.6 Off-Policy Monte Carlo Control 5.7 Incremental Implementation 5.8 Summary 5.9 Bibliographical and Historical Remarks 6. Temporal-Difference Learning 6.1 TD Prediction 6.2 Advantages of TD Prediction Methods 6.3 Optimality of TD(0) 6.4 Sarsa: On-Policy TD Control 6.5 Q-Learning: Off-Policy TD Control 6.6 Actor-Critic Methods 6.7 R-Learning for Undiscounted Continuing Tasks 6.8 Gam

David J. C. MacKay's book. The textbook used for the information theory course in Cambridge University. Also an excellent book for machine learning. The special feature of this book is it reveals the relationship between information theory and machine learning. According to the author "information and learning are the two sides of the same coin". This book is strongly recommended for those studying machine learning and/or information theory. It really brainstorms you. Contents Preface 1 Introduction to Information Theory 2 Probability, Entropy, and Inference 3 More about Inference I Data Compression 4 The Source Coding Theorem 5 Symbol Codes 6 Stream Codes 7 Codes for Integers II Noisy-Channel Coding 8 Dependent Random Variables 9 Communication over a Noisy Channel 10 The Noisy-Channel Coding Theorem 11 Error-Correcting Codes and Real Channels III Further Topics in Information Theory 12 Hash Codes: Codes for Ecient Information Retrieval 13 Binary Codes 14 Very Good Linear Codes Exist 15 Further Exercises on Information Theory 16 Message Passing 17 Communication over Constrained Noiseless Channels 18 Crosswords and Codebreaking 19 Why have Sex? Information Acquisition and Evolution IV Probabilities and Inference 20 An Example Inference Task: Clustering 21 Exact Inference by Complete Enumeration 22 Maximum Likelihood and Clustering 23 Useful Probability Distributions 24 Exact Marginalization 25 Exact Marginalization in Trellises 26 Exact Marginalization in Graphs 27 Laplace's Method 28 Model Comparison and Occam's Razor 29 Monte Carlo Methods 30 Ecient Monte Carlo Methods 31 Ising Models 32 Exact Monte Carlo Sampling 33 Variational Methods 34 Independent Component Analysis and Latent Variable Modelling 35 Random Inference Topics 36 Decision Theory 37 Bayesian Inference and Sampling Theory V Neural networks 38 Introduction to Neural Networks 39 The Single Neuron as a Classier 40 Capacity of a Single Neuron 41 Learning as Inference 42 Hopeld Networks 43 Boltzmann Machines 44 Supervised Learning in Multilayer Networks 45 Gaussian Processes 46 Deconvolution VI Sparse Graph Codes 47 Low-Density Parity-Check Codes 48 Convolutional Codes and Turbo Codes 49 Repeat{Accumulate Codes 50 Digital Fountain Codes VII Appendices A Notation B Some Physics C Some Mathematics Bibliography Index

Contents: Preface 1 Introduction 1.1 Computer Graphics 1.2 Abstraction Paradigms 1.3 About This Book 1.4 Comments and References 2 Signal Theory 2.1 Abstraction Paradigms 2.2 Mathematical Models for Signals 2.3 Linear Representation of Signals 2.4 Operations on Signals 2.5 Sampling Theory 2.6 Operations in the Discrete Domain 2.7 The Inverse Discrete Transform 2.8 The Discrete Transform on the Interval [0,A] 2.9 Matrix Representation of the DFT 2.10 The Fast Fourier Transform 2.11 Finite Transform 2.12 Comments and References 3 Random Processes 3.1 Random Variables 3.2 Stochastic Processes 3.3 Point Processes 3.4 Comments and References 4 Fundamentals of Color 4.1 Paradigms in the Study of Color 4.2 The Physical Universe of Color 4.3 The Mathematical Universe of Color 4.4 The Representation Universe of Color 4.5 CIE-RGB Representation 4.6 Luminance and Chrominance 4.7 The Color Solid 4.8 Grassmann’s Laws 4.9 Comments and References 5 Color Systems 5.1 Preliminary Notions 5.2 Changing Between Color Systems 5.3 Color Systems and Computer Graphics 5.4 Standard Color Systems 5.5 Device Color Systems 5.6 Color Interface Systems 5.7 Computational Color Systems 5.8 Color Transformations 5.9 Comments and References 6 Digital Images 6.1 Abstraction Paradigms for Images 6.2 The Spatial Model 6.3 Comments and References 7 Operations on Images 7.1 Arithmetic Operations 7.2 Filters 7.3 Spatially Invariant Linear Filters 7.4 Examples of Linear Filters 7.5 Edge Enhancement Operations 7.6 Comments and References 8 Sampling and Reconstruction 8.1 Sampling 8.2 Reconstruction 8.3 Aliasing 8.4 Reconstruction Problems 8.5 Some Classical Reconstruction Filters 8.6 A Study of Reconstruction Problems 8.7 Reconstructing After Aliasing 8.8 A Case Study 8.9 Comments and References 9 Multiscale Analysis and Wavelets 9.1 The Wavelet Transform 9.2 The Discrete Wavelet Transform 9.3 Multiresolution Representation 9.4 Multiresolution Representation and Wavelets 9.5 The Fast Wavelet Transform 9.6 Wavelet Decomposition and Reconstruction 9.7 The Fast Wavelet Transform Algorithm 9.8 Images and 2D-Wavelets 9.9 Comments and References 10 Probabilistic Image Models 10.1 Image Formation 10.2 Observed Data 10.3 Histograms and Estimation 10.4 Correlated Observations 10.5 Filtering 10.6 Classes 10.7 Comments and References 11 Color Quantization 11.1 Quantization Cells 11.2 Quantization and Perception 11.3 Quantization Error 11.4 Uniform and Adaptive Quantization 11.5 Adaptive Quantization Methods 11.6 Optimization Methods for Quantization 11.7 Optimal One-Dimensional Quantization 11.8 Optimal Quantization by Relaxation 11.9 Comments and References 12 Digital Halftoning 12.1 Dithering 12.2 Periodic Dithering 12.3 Pattern Dithering 12.4 Nonperiodic Dithering 12.5 Comments and References 13 Image Compression 13.1 Image Encoding 13.2 Image Compression XIV Contents 13.3 Compression and Multiscale Analysis 13.4 Comments and References 14 Combining Images 14.1 Preliminaries 14.2 Combining Images Algebraically 14.3 Combining Images by Decomposing the Domain 14.4 Combining Images in the Discrete Domain 14.5 Computation of the Opacity Function 14.6 Compositing in the Discrete Domain 14.7 Compositing Operations 14.8 Comments and References 15 Warping and Morphing 15.1 Warping Filters 15.2 Warping in the Continuous Domain 15.3 Warping in the Discrete Domain 15.4 Some Examples 15.5 Warping in Practice 15.6 Morphing 15.7 Continuous Families of Transformations 15.8 Comments and References 16 Image Systems 16.1 Image Characteristics 16.2 Image Display 16.3 Cross Rendering 16.4 Color Correction 16.5 Display Models 16.6 Electronic Publishing Systems 16.7 Comments and References A Appendix: Radiometry and Photometry A.1 Radiometry A.2 Photometric Variables A.3 Comments and References Index

An excellent book for state-of-art analog VLSI design technology. Recommended for those who are interested in the coming revolution of digital-analog hybrid circuits design and neuromorphic engineering technology. Content Authors and Contributors xiii Acknowledgments xv Preface xvii Foreword xix 1 Introduction I SILICON AND TRANSISTORS 2 Semiconductor Device Physics 2.1 Crystal Structure 2.2 Energy Band Diagrams 2.3 Carrier Concentrations at Thermal Equilibrium 2.4 Impurity Doping 2.5 Current Densities 2.6 p-n Junction Diode 2.7 The Metal-Insulator-Semiconductor Structure 3 MOSFET Characteristics 3.1 MOSFET Structure 3.2 Current–Voltage Characteristics of an nFET 3.3 Current–Voltage Characteristics of a pFET 3.4 Small-Signal Model at Low Frequencies 3.5 Second-Order Effects 3.6 Noise and Transistor Matching 3.7 Appendices 4 Floating-Gate MOSFETs 4.1 Floating-Gate MOSFETs 4.2 Synapse Transistors 4.3 Silicon Learning Arrays 4.4 Appendices II STATICS 5 Basic Static Circuits - J¨org Kramer 5.1 Single-Transistor Circuits 5.2 Two-Transistor Circuits 5.3 Differential Pair and Transconductance Amplifier 5.4 Unity-Gain Follower 6 Current-Mode Circuits 6.1 The Current Conveyor 6.2 The Current Normalizer 6.3 Winner-Take-All Circuits 6.4 Resistive Networks 6.5 Current Correlator and Bump Circuit 7 Analysis and Synthesis of Static Translinear Circuits 7.1 The Ideal Translinear Element 7.2 Translinear Signal Representations 7.3 The Translinear Principle 7.4 ABC’s of Translinear-Loop–Circuit Synthesis 7.5 The Multiple-Input Translinear Element 7.6 Multiple-Input Translinear Element Networks 7.7 Analysis of MITE Networks 7.8 ABC’s of MITE-Network Synthesis III DYNAMICS 8 Linear Systems Theory - Giacomo Indiveri 8.1 Linear Shift-Invariant Systems 8.2 Convolution 8.3 Impulses 8.4 Impulse Response of a System 8.5 Resistor-Capacitor Circuits 8.6 Higher Order Equations 8.7 The Heaviside-Laplace Transform 8.8 Linear System’s Transfer Function 8.9 The Resistor-Capacitor Circuit (A Second Look) 8.10 Low-Pass, High-Pass, and Band-Pass Filters 9 Integrator-Differentiator Circuits 9.1 The Follower-Integrator 9.2 The Current-Mirror Integrator 9.3 The Capacitor 9.4 The Follower-Differentiator Circuit 9.5 The diff1 and diff2 Circuits 9.6 Hysteretic Differentiators 10 Photosensors 10.1 Photodiode 10.2 Phototransistor 10.3 Photogate 10.4 Logarithmic Photosensors 10.5 Imaging Arrays 10.6 Limitations Imposed by Dark Current on Photosensing IV SPECIAL TOPICS 11 Noise in MOS Transistors and Resistors 11.1 Noise Definition 11.2 Noise in Subthreshold MOSFETs 11.3 Shot Noise versus Thermal Noise 11.4 The Equipartition Theorem and Noise Calculations 11.5 Noise Examples 12 Layout Masks and Design Techniques 12.1 Mask Layout for CMOS Fabrication 12.2 Layout Techniques for Better Performance 12.3 Short List of Matching Techniques 12.4 Parasitic Effects 12.5 Latchup 12.6 Substrate Coupling 12.7 Device Matching Measurements 13 A Millennium Silicon Process Technology 13.1 A typical 0.25 m CMOS Process Flow 13.2 Scaling Limits for Conventional Planar CMOS Architectures 13.3 Conclusions and Guidelines for New Generations 14 Scaling of MOS Technology to Submicrometer Feature Sizes 14.1 Scaling Approach 14.2 Threshold Scaling 14.3 Device Characteristics 14.4 System Properties 14.5 Conclusions Appendix A: Units and symbols References Index