On: Artificial Intelligence: A Modern Approach

[paraschopra]

While the book is good, it lacks the statistical approach to AI and ML. Currently, most of the AI or ML is done in statistical fashion and I think this book does not do justice in introducing those topics well.

[schtog → paraschopra]

? chater 5 to 7 seem to cover that pretty well.

Part V Uncertain Knowledge and Reasoning 13 Uncertainty 14 Probabilistic Reasoning 15 Probabilistic Reasoning Over Time 16 Making Simple Decisions 17 Making Complex Decisions Part VI Learning 18 Learning from Observations 19 Knowledge in Learning 20 Statistical Learning Methods (pdf) 21 Reinforcement Learning Part VII Communicating, Perceiving, and Acting 22 Communication 23 Probabilistic Language Processing 24 Perception 25 Robotics Part VIII Conclusions 26 Philosophical Foundations 27 AI: Present and Future Bibliography (pdf and counts) Index (html or pdf)

[vang3lis → paraschopra]

what would you recommend to supplement Russel&Norvig?

[plinkplonk → vang3lis]

"what would you recommend to supplement Russel&Norvig?"

Elements Of Statistical learning( http://www-stat.stanford.edu/~tibs/ElemStatLearn/ )

[paraschopra → vang3lis]

I would rather recommend Machine Learning video course from Stanford by Andrew Ng.

Pattern Recognition and Machine Learning by Christopher M. Bishop is good too

[jacoblyles → vang3lis]

Everybody I meet in industry and academia has a copy of Bishop's "Pattern Recognition and Machine Learning". It's pretty comprehensive.

The most recent version of Russell and Norvig does have a solid statistics section in it, but they don't start it until the middle of the book.

[mrduncan → vang3lis]

My vote goes to Programming Collective Intelligence (http://oreilly.com/catalog/9780596529321/)