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Artificial Intelligence: A Modern Approach
- jlees 18y agoA classic but I'm not quite sure why it was posted? Initially thought they'd made the text available online, which would be cool. But alas. Is the Google Code or design-a-cover thing the actual news here?
- ricree 18y agoAgreed, although the code examples in python ( http://code.google.com/p/aima-python/ http://code.google.com/p/aima-python/ ) and lisp ( http://aima.cs.berkeley.edu/lisp/doc/overview.html http://aima.cs.berkeley.edu/lisp/doc/overview.html ) are worth pointing out.
- dmix 18y agoDoes AI + Python have any practical use in production? Most of the algorithms I've tested seem pretty slow. Although the linked examples are for learning. From my research AI seems to be primarily LISP in academia and C in production.
- marcus 18y agoI actually use a blend of C & Python, C for doing the heavy lifting and Python for the rapid prototyping. Most AI algorithms have clearly defined segments that are very CPU intensive, by only using C in those areas I get the optimal balance of development speed and execution speed.
- arjunnarayan 18y agoEver since I learnt Cython, this has been my preferred combination as well. I can't overstate enough how great it is to be able to get the rapid prototyping of Python combined with being able to offload the heavy stuff to C.
- lliiffee 18y agoIncreasingly these days academic AI is done in Matlab or similar systems.
- plinkplonk 18y ago"A classic but I'm not quite sure why it was posted?" and why is it getting so many upvotes? Extending this approach, if anyone is looking for free karma, just post separate links to all the classic CS books, without any comments or added explanation (3 separate submissions for the TAOCP Knuth books ;-)). Lots of karma for the taking ;-) now that this is on the front page, here is something interesting. There's a 1000 $ prize for designing a cover for the third edition (see top right hand corner of teh page). The cover of the first two editions are very dense with references to AI history. If I could draw worth a damn I would have taken a shot at it.
- jjames 18y agoSerious tease. I expected either the text available online, lecture videos or at the very least a courseware podcast from Berkeley. I guess I'll go back to staring at it at the bookstore...
- DTrejo 18y agoSign up for Gigapedia.org and you can get it. Email me if you have questions about how to DL it.
- Eliezer 18y agoMy name is Eliezer Yudkowsky, and I think this book is awesome.
- sgrove 18y agoI love this book as well, and I make sure to read it over regularly to spark different ideas - but it's expensive. Buying it was a bitterweet experience, to say the least. Owell, it's at least the best you're likely to find.
- mahmud 18y agoIt bored me to tears. The "agent" orientation of the book does not address my needs for AI: data mining and inference. Norvig's companion Paradigms of Artificial Intelligence was a bliss however :-)
- vang3lis 18y agoMy name is vang3lis and I'm an alcoholic. So what?
- fizz 18y agoI like the AI Game Programming Wisdom Series (http://www.aiwisdom.com/ http://www.aiwisdom.com/). Or maybe if we are posting up classics we could mention The Society of Mind by Marvin Minsky.
- benkant 18y agoIt appears a 3rd edition is on the way. I wonder what changes it will bring? http://media.pearsoncmg.com/ph/esm/ecs_ai/ http://media.pearsoncmg.com/ph/esm/ecs_ai/
- paraschopra 18y agoWhile 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 18y ago? 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 18y agowhat would you recommend to supplement Russel&Norvig?
- plinkplonk 18y ago"what would you recommend to supplement Russel&Norvig?" Elements Of Statistical learning( http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ )
- jacoblyles 18y agoEverybody 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.
- paraschopra 18y agoI would rather recommend Machine Learning video course from Stanford by Andrew Ng. Pattern Recognition and Machine Learning by Christopher M. Bishop is good too
- aufreak 18y agoThe ToC doesn't contain anything about the situated cognition, enactive cognition schools of thought, so I find applying the term AI to this book too broad. The topics covered seems to broadly fall under "automatic problem solving strategies" or something like that. Check out Rodney Brooks' robotics work for anything that feels like intelligence.
- zandorg 18y agoI sold my copy on Amazon when I realised I'd get all my money back.