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] | ||
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== Resources == | == Resources == | ||
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== Topics == | == Topics == | ||
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| + | *Philosophy of Mind (lecture 10) | ||
| + | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture10/Chap26.pdf Reading] | ||
| + | |||
| + | *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) | *IBM Watson (lecture 7) | ||
| − | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/watson/ Articles] | + | **[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: | **Read: | ||
| − | ***Introduction | + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/01Introduction.pdf Introduction] |
| − | ***Identifying Implicit Relationships | + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/12IdentifyImplicitRelationships.pdf Identifying Implicit Relationships] |
| − | ***Deep Parsing | + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/03DeepParsing.pdf Deep Parsing] |
| − | ***Typing | + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/07Typing.pdf Typing] |
| − | ***Automatic Knowledge Extraction | + | ***[http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture7/watson/05AutomaticKnowledgeExtraction.pdf Automatic Knowledge Extraction] --> |
| + | |||
*Hidden Markov Models (lecture 6) | *Hidden Markov Models (lecture 6) | ||
** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture6/notes.txt Lecture notes] | ** [http://harold.uits.indiana.edu/~jtillots/AI-class/lectures/lecture6/notes.txt Lecture notes] | ||
| − | ** [http://www.cs.ubc.ca/~murphyk/Bayes/rabiner. | + | ** [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://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://sifaka.cs.uiuc.edu/course/498cxz05f/hmm.pdf Zhai Tutorial] | ||
** [http://en.wikipedia.org/wiki/Viterbi_algorithm Viterbi Algorithm] | ** [http://en.wikipedia.org/wiki/Viterbi_algorithm Viterbi Algorithm] | ||
** [http://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm Baum-Welch 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) | *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) | *Perceptrons/Neural Networks (lecture 3 and 4) | ||
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)
