“Practical Artificial Intelligence Programming With Java” by Mark Watson(leanpub.com)

  Introduction 
  # Other JVM Languages 
  # Github Repository for Book Software 
  # Use of Java Generics and Native Types 
  # Notes on Java Coding Styles Used in this Book 
  # Book Summary 
  
  Search 
  # Representation of Search State Space and Search Operators 
  # Finding Paths in Mazes 
  # Finding Paths in Graphs 
  # Adding Heuristics to Breadth First Search 
  # Search and Game Playing 
  
  Reasoning 
  # Logic 
  # PowerLoom Overview 
  # Running PowerLoom Interactively 
  # Using the PowerLoom APIs in Java Programs 
  # Suggestions for Further Study 
  
  Semantic Web 
  # Relational Database Model Has Problems Dealing with Rapidly Changing Data Requirements 59
  # RDF: The Universal Data Format 
  # Extending RDF with RDF Schema 
  # The SPARQL Query Language 
  # Using Sesame 
  # OWL: The Web Ontology Language 
  # Knowledge Representation and REST 
  # Material for Further Study 
  
  Expert Systems 
  # Production Systems 
  # The Drools Rules Language 
  # Using Drools in Java Applications 
  # Example Drools Expert System: Blocks World 
  # Example Drools Expert System: Help Desk System 
  # Notes on the Craft of Building Expert Systems 
  
  Genetic Algorithms 
  # Theory 
  # Java Library for Genetic Algorithms 
  # Finding the Maximum Value of a Function 
  
  Machine Learning with Weka 
  # Using Weka’s Interactive GUI Application 
  # Interactive Command Line Use of Weka 
  # Embedding Weka in a Java Application 
  # Suggestions for Further Study 
  
  Neural Networks 
  # Hopfield Neural Networks 
  # Java Classes for Hopfield Neural Networks 
  # Testing the Hopfield Neural Network Class 
  # Back Propagation Neural Networks 
  # A Java Class Library for Back Propagation 
  # Adding Momentum to Speed Up Back-Prop Training 
  
  Statistical Natural Language Processing 
  # Tokenizing, Stemming, and Part of Speech Tagging Text 
  # Named Entity Extraction From Text 
  # Using the WordNet Linguistic Database 
  # Automatically Assigning Tags to Text 
  # Text Clustering 
  # Spelling Correction 
  # Hidden Markov Models 
  
  Information Gathering 
  # Open Calais 
  # Information Discovery in Relational Databases 
  # Down to the Bare Metal: In-Memory Index and Search 
  # Indexing and Search Using Embedded Lucene 
  # Indexing and Search with Nutch Clients 
  
  Data Science Techniques 
  # A Mix of Open Source and Proprietary Tools 
  # Handling “small big data” in a Cost Effective Way 
  # Writing and Testing MapReduce Applications 
  # Example Application: MapReduce Application for Finding Proper Names in Text 
  # Using Inexpensive Large Memory Leased Servers 
  # Example Application Idea: Using the Google Book Project NGRAM Data Sets 
  # Example Application Idea: Using Wikipedia Data Dumps 
  # Conclusion 
  
  Conclusions