[lpolovets]

I just bought the book (at $6, it is an easy impulse buy...)

Table of contents:

  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

[wiradikusuma]

This is stupid question, but based on the ToC, is this book suitable for Java programmer who doesn't know anything about AI other than from Hollywood movies?

[eli_gottlieb]

Almost definitely not, from the look of it. Even the first few chapters go beyond my old CS387 Intro to AI class in college.

I feel like now that I'm in grad school I should take more and better AI courses, but I just can't find the interest in me, no matter that the field is economically hot right now.

[mark_l_watson]

Thanks for posting that! And, of course, thanks for buying a copy of my book.

[FraaJad]

Could you please paste this under "Table of Contents" of the book page? That would help people decide.

[zura]

This looks like a pretty broad coverage book. I wonder if the book has same issues as other broad books - they do good in telling you what topics exist but fail to go in depth.

[mark_l_watson]

Hello Zura, that is a fair comment. I don't go into a lot of depth for some topics. I introduce an idea, usually explore an idea with some code, and try to suggest possible projects. If you want a thorough and deep coverage of AI, work through http://aima.cs.berkeley.edu/