Difference between revisions of "AIClass"
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* [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures Class lectures] | * [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures Class lectures] | ||
| − | + | == Resources == | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | * [http://inst.eecs.berkeley.edu/~cs188/fa11/lectures.html Berkeley course] | |
| + | * [http://videolectures.net/ http://videolectures.net/] | ||
| − | == | + | == Topics == |
| − | + | ||
| − | *Genetic Algorithms | + | *Philosophy of Mind (lecture 10) |
| − | ** http://burakkanber.com/blog/machine-learning-genetic-algorithms-part-1-javascript/ | + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture10/Chap26.pdf Reading] |
| − | ** http://burakkanber.com/blog/machine-learning-genetic-algorithms-in-javascript-part-2/ | + | |
| + | *Learning (lecture 9) | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture9/Chap20-21.pdf Reading] | ||
| + | |||
| + | *Vision (lecture 8) | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture8/chap24.pdf Reading] | ||
| + | ** [http://ninedegreesbelow.com/photography/all-the-colors.html RBG Color Gamuts] | ||
| + | ** [http://homepage.cs.uiowa.edu/~cwyman/classes/spring08-22C251/homework/canny.pdf Canny Edge Detection Algorithm] | ||
| + | |||
| + | *IBM Watson (lecture 7) | ||
| + | **[http://www.slideshare.net/jahendler/watson-summer-review82013final Watson at RPI (Slide Presentation)] | ||
| + | **[http://nlp.cs.rpi.edu/course/spring14/nlp.html Open Source Watson] | ||
| + | **[https://mu.lti.cs.cmu.edu/trac/oaqa CMU's OAQA (Open Advancement of Question Answering)] | ||
| + | **[https://www.ibm.com/developerworks/community/blogs/InsideSystemStorage/entry/ibm_watson_how_to_build_your_own_watson_jr_in_your_basement7?lang=en How to build your own "Watson Jr." in your basement] | ||
| + | <!-- **[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/ Articles] | ||
| + | **Read: | ||
| + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/01Introduction.pdf Introduction] | ||
| + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/12IdentifyImplicitRelationships.pdf Identifying Implicit Relationships] | ||
| + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/03DeepParsing.pdf Deep Parsing] | ||
| + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/07Typing.pdf Typing] | ||
| + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/05AutomaticKnowledgeExtraction.pdf Automatic Knowledge Extraction] --> | ||
| + | |||
| + | *Hidden Markov Models (lecture 6) | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture6/notes.txt Lecture notes] | ||
| + | ** [http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf Rabiner Tutorial] | ||
| + | ** [http://www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf Stamp Review of Rabiner] | ||
| + | ** [http://sifaka.cs.uiuc.edu/course/498cxz05f/hmm.pdf Zhai Tutorial] | ||
| + | ** [http://en.wikipedia.org/wiki/Viterbi_algorithm Viterbi Algorithm] | ||
| + | ** [http://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm Baum-Welch Algorithm] | ||
| + | ** [http://nlp.stanford.edu/fsnlp/ Foundations of Statistical Natural Language Processing] | ||
| + | |||
| + | *Bayesian Networks (lecture 4 and 5) | ||
| + | ** [http://inst.eecs.berkeley.edu/~cs188/fa11/slides/FA11%20cs188%20lecture%2014%20--%20bayes%20nets%20II%20(2PP).pdf Bayesian I] | ||
| + | ** [http://inst.eecs.berkeley.edu/~cs188/fa11/slides/FA11%20cs188%20lecture%2015%20--%20bayes%20nets%20III%20(2PP).pdf Bayesian II] | ||
| + | ** [http://www.cs.cmu.edu/~ggordon/10601/hws/hw2/hw2.pdf Homework] | ||
| + | ** [http://www.cs.cmu.edu/~ggordon/10601/hws/hw2/hw2_sol.pdf Solutions] | ||
| + | |||
| + | *Perceptrons/Neural Networks (lecture 3 and 4) | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture3/lecture3.txt Topics and Readings] | ||
| + | ** [http://page.mi.fu-berlin.de/rojas/neural/chapter/ Rojas' online Neural Network book] | ||
| + | ** [http://www.stanford.edu/group/pdplab/pdphandbook/handbook.pdf Explorations in Parallel Distributed Processing] | ||
| + | ** [http://www.stanford.edu/group/pdplab/pdphandbook/ Online version of Explorations in PDP] | ||
| + | ** [http://www.stanford.edu/group/pdplab/resources.html#pdptool PDP software] | ||
| + | ** [http://channel9.msdn.com/Events/Build/2013/2-401 Video on developing neural networks] | ||
| + | |||
| + | *Genetic Algorithms (lecture 2) | ||
| + | ** Readings | ||
| + | *** http://burakkanber.com/blog/machine-learning-genetic-algorithms-part-1-javascript/ | ||
| + | *** http://burakkanber.com/blog/machine-learning-genetic-algorithms-in-javascript-part-2/ | ||
** Online examples | ** Online examples | ||
*** http://www.rennard.org/alife/english/gavgb.html | *** http://www.rennard.org/alife/english/gavgb.html | ||
*** https://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html | *** https://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture2/GAs Examples from class] | ||
| + | ** Homework | ||
| + | *** Implement an algorithm that solves the knapsack problem | ||
| + | *** See the second reading for a description of the problem | ||
| + | *** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture2/knapsack.c Data and data structure] | ||
| − | + | *Search (lecture 1) | |
| − | + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture1/lecture1.txt Topics and Readings] | |
| − | * [http:// | + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture1 Slides] |
| − | * [http:// | + | ** Homework |
| + | *** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture1/tictactoe Minimax/AlphaBeta Pruning] | ||
Latest revision as of 00:19, 6 February 2015
[edit] Information
- Class mailing list: AI-class@bloominglabs.org
[edit] Resources
[edit] Topics
- Philosophy of Mind (lecture 10)
- Learning (lecture 9)
- Vision (lecture 8)
- IBM Watson (lecture 7)
- Hidden Markov Models (lecture 6)
- Bayesian Networks (lecture 4 and 5)
- Perceptrons/Neural Networks (lecture 3 and 4)
- Genetic Algorithms (lecture 2)
- Readings
- Online examples
- Examples from class
- Homework
- Implement an algorithm that solves the knapsack problem
- See the second reading for a description of the problem
- Data and data structure
- Search (lecture 1)
