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Wrap Up Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001.

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Presentation on theme: "Wrap Up Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001."— Presentation transcript:

1 Wrap Up Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001

2 Administration Laptop four is busted. Course evaluations.

3 Plan LSI and autoassociation Topic list Second half Themes Wrap up

4 Passage Autoassociator x1x1x1x1 h1h1h1h1 x2x2x2x2 x3x3x3x3 x4x4x4x4 h2h2h2h2 U (nxk) U T (kxn) x1x1x1x1 x2x2x2x2 x3x3x3x3 x4x4x4x4 word features capture “gist” so it can be reconstructed

5 We’ll Make Computers Brighter (sorry Billy Joel) Gang and Kedar relaxation color graphs by propagation Neighbor goal state Davis Putnam change to CNF CSP & satisfaction forward checking giving traction BFS and DFS and A* is the best

6 Verse 2 Local search save time optimize hill climb Nim Sharks GSAT chess evaluation hacks Alpha beta game tree prune the branch and you’ll see Fitness function reproduction phase transition min-max

7 Chorus We’ll make computers brighter With their “and” and “or”ing They’ll beat Alan Turing. We’ll make computers brighter Smarter than a porpoise Train it with a corpus

8 Verse 3 Markov model Chinese Room Nim-Rand in a matrix form Labeled data, missing data logic of Boole Discount rank list Altavista Wordnet IR bag-of-words flip around with Bayes Rule

9 Verse 4 Rap song n-queen Susan swallowed something green Language model classify take a max and multiply Zipf’s law trigrams smooth it out with bigrams Naïve Bayes winning plays write another program

10 Chorus We’ll make computers brighter With their “and” and “or”ing They’ll beat Alan Turing. We’ll make computers brighter Smarter than a porpoise Train it with a corpus

11 Verse 5 HMM part-of-speech they will learn if you will teach Baum-Welch if you please Rush Hour and Analogies Weighted coins network lags Viterbi finds most likely tags Start running watch for ties expectation maximize

12 Verse 6 Learning function feature try learning who’s a girl overfit PAC bounds build your own decision tree Too big memorize cross validate to generalize supervising pure leaf fit the function to a ‘t’

13 Chorus We’ll make computers brighter With their “and” and “or”ing They’ll beat Alan Turing. We’ll make computers brighter Smarter than a porpoise Train it with a corpus

14 Verse 7 Delta rule backprop hidden unit don’t stop Learn pairs sum squares linear perceptron Error surface is quite bent just descend the gradient and-or sigmoid nonlinear regression

15 Bridge/Chorus XOR learning rate squash the sum and integrate Neural net split the set How much better can it get? Chorus: We’ll make computers brighter With their “and” and “or”ing They’ll beat Alan Turing. We’ll make computers brighter Smarter than a porpoise Train it with a corpus

16 Verse 8 LSI max and min local search is back again TOEFL pick best essay grade pass the test Matrix Plato SVD vectors all in high D All the points are in the space don’t you make an Eigenface!

17 Verse 9 Segmentation Saturn spin Probabilities a win Q and P iteratively Bayes net CPT Autopilots in the sky Play chess masters for a tie Alan Alda robots cry All of this is in AI!

18 Chorus We’ll make computers brighter With their “and” and “or”ing They’ll beat Alan Turing. We’ll make computers brighter Smarter than a porpoise Train it with a corpus

19 Final Chorus We’ll make computers brighter Hope you had some fun Because the class is done, is done, is done…

20 Probability Models Compare and contrast unigramunigram bigrambigram trigramtrigram HMMHMM belief netbelief net Naïve BayesNaïve Bayes

21 Classification Algs Compare and contrast Naïve BayesNaïve Bayes decision treedecision tree perceptronperceptron multilayer neural netmultilayer neural net

22 Missing Data Compare and contrast EMEM SVDSVD gradient descentgradient descent

23 Dynamic Programming Compare and contrast ViterbiViterbi forward-backwardforward-backward minimaxminimax Markov chains (value iteration)Markov chains (value iteration) segmentationsegmentation

24 Big Picture: This is AI? We learned a bunch of programming tricks: people still do the hard work. Yeah, that’s true.Yeah, that’s true. It’s not such a bad thing: building more flexible software (search, learning).It’s not such a bad thing: building more flexible software (search, learning). We’re working towards more independent artifacts; not there yetWe’re working towards more independent artifacts; not there yet

25 Thank You! Thanks for your help! Gang and KedarGang and Kedar Lisa and the kidsLisa and the kids AT&TAT&T Andrew MooreAndrew Moore Family Feud survey respondentsFamily Feud survey respondents Peter StonePeter Stone all of you!all of you!


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