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  2. [12]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
    Thumbs Up
    Received: 7
    Given: 0
    Machine Learning Theory (Weizmann course, Fall 2006-7)

    In theory, this course will cover models and algorithms for machine learning. What classes of functions are learnable and what algorithms should be used? Where possible, we will try to prove appealing properties of algorithms that are also useful in practice. This course will also demystify many buzzwords such as: PAC learning, VC dimension, Boosting, Fourier Techniques, Statistical Queries, Parity with Noise, Decision Tree learning, Support Vector Machines, and the Multi-Armed Bandit Problem.

    Note: Another course being offered this semester is Probabilistic Graphical Models by Eran Segal. His course is disjoint from this course and would be excellent to take in conjunction. Course info

    Location: Weizmann Institute, Ziskind Room 1
    Time: Tue. 14:00-16:00, Oct. 31, 2006-Feb. 6, 2007
    Instructor: Adam Tauman Kalai.
    Grader: Ariel Gabizon.

    This course is partly based on an excellent Machine Learning Theory course taught by Avrim Blum at CMU.
    Mailing list

    The course mailing list is here. You can join or simply read the messages.
    Recommended textbooks

    Michael Kearns and Umesh Vazirani. An introduction to computational learning theory.
    Tom Mitchell. Machine Learning. (More applied)
    Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning. (More statistics-oriented)
    Homework

    Homework should be turned in either in class or may be put in the MACHINE LEARNING course mail box, which is on the second floor of Ziskind.
    Problem set 1 is now available. Due Nov. 28, 2006. Problem set 1 hint is now available.
    Problem set 2 is now available. Due Dec. 14, 2006.
    Problem set 3, theory or implementation is now available. You have your choice. Due Jan. 11, 2007.
    Problem set 4 is now available. Due Feb. 8, 2007. (Clarifications added 8/2/07.)
    Lectures

    This schedule is certain to change.
    Introduction and online learning

    Oct 31. Introduction. Learning an OR via the WINNOW algorithm, and online adaptation. An interesting article, The discipline of machine learning by Tom Mitchell.
    Nov 7. The Weighted Majority and Perceptron algorithms. An interesting related paper is the following:
    Smoothed Analysis of the Perceptron Algorithm for Linear Programming by Avrim Blum and John Dunagan. Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, (SODA 2002), pp. 905-914, 2002.
    Nov 14. Holiday.
    Batch learning

    Nov 21. Batch learning, online to batch conversion, and Occams razor. See also the handout on tail inequalities from Avrim Blum. Slides are available here.
    Nov 28. VC dimension. Slides are available.
    Dec 5. No class. See interesting related lecture on Sunday, Dec. 10.
    Dec 12. Boosting: AdaBoost. Here is a survey of boosting, and some excellent slides by Schapire.
    Dec 19. Decision trees and Noisy/Real boosting. Slides are available. Note, course meets in Ziskind 261.
    Dec 26. Completion of real-valued boosting and decision regression graphs, and additive models. Last weeks notes plus this weeks slides cover this week.
    Jan 2. Class canceled.
    Jan 9. Statistical queries and learning parity with noise.
    Jan 16. Support vector machines and fourier methods. We will use slides by Martin Law.
    Jan 23. SVMs, Fourier, and Agnostic Learning.
    Other models of learning

    Jan 30. Online optimization. See paper by Zinkevich. For extension to the bandit setting, see this paper.
    Feb 6. Learning in repeated games. See Regret in the On-line Decision Problem by Foster and Vohra for a great introduction. To see the specific reduction from external to internal regret I was talking about, see From External to Internal Regret by Avrim Blum and Yishay Mansour.

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  3. [13]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
    Thumbs Up
    Received: 7
    Given: 0
    Machine Learning Theory (Weizmann course, Fall 2006-7)

    In theory, this course will cover models and algorithms for machine learning. What classes of functions are learnable and what algorithms should be used? Where possible, we will try to prove appealing properties of algorithms that are also useful in practice. This course will also demystify many buzzwords such as: PAC learning, VC dimension, Boosting, Fourier Techniques, Statistical Queries, Parity with Noise, Decision Tree learning, Support Vector Machines, and the Multi-Armed Bandit Problem.

    Note: Another course being offered this semester is Probabilistic Graphical Models by Eran Segal. His course is disjoint from this course and would be excellent to take in conjunction. Course info

    Location: Weizmann Institute, Ziskind Room 1
    Time: Tue. 14:00-16:00, Oct. 31, 2006-Feb. 6, 2007
    Instructor: Adam Tauman Kalai.
    Grader: Ariel Gabizon.

    This course is partly based on an excellent Machine Learning Theory course taught by Avrim Blum at CMU.
    Mailing list

    The course mailing list is here. You can join or simply read the messages.
    Recommended textbooks

    Michael Kearns and Umesh Vazirani. An introduction to computational learning theory.
    Tom Mitchell. Machine Learning. (More applied)
    Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning. (More statistics-oriented)
    Homework

    Homework should be turned in either in class or may be put in the MACHINE LEARNING course mail box, which is on the second floor of Ziskind.
    Problem set 1 is now available. Due Nov. 28, 2006. Problem set 1 hint is now available.
    Problem set 2 is now available. Due Dec. 14, 2006.
    Problem set 3, theory or implementation is now available. You have your choice. Due Jan. 11, 2007.
    Problem set 4 is now available. Due Feb. 8, 2007. (Clarifications added 8/2/07.)
    Lectures

    This schedule is certain to change.
    Introduction and online learning

    Oct 31. Introduction. Learning an OR via the WINNOW algorithm, and online adaptation. An interesting article, The discipline of machine learning by Tom Mitchell.
    Nov 7. The Weighted Majority and Perceptron algorithms. An interesting related paper is the following:
    Smoothed Analysis of the Perceptron Algorithm for Linear Programming by Avrim Blum and John Dunagan. Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, (SODA 2002), pp. 905-914, 2002.
    Nov 14. Holiday.
    Batch learning

    Nov 21. Batch learning, online to batch conversion, and Occams razor. See also the handout on tail inequalities from Avrim Blum. Slides are available here.
    Nov 28. VC dimension. Slides are available.
    Dec 5. No class. See interesting related lecture on Sunday, Dec. 10.
    Dec 12. Boosting: AdaBoost. Here is a survey of boosting, and some excellent slides by Schapire.
    Dec 19. Decision trees and Noisy/Real boosting. Slides are available. Note, course meets in Ziskind 261.
    Dec 26. Completion of real-valued boosting and decision regression graphs, and additive models. Last weeks notes plus this weeks slides cover this week.
    Jan 2. Class canceled.
    Jan 9. Statistical queries and learning parity with noise.
    Jan 16. Support vector machines and fourier methods. We will use slides by Martin Law.
    Jan 23. SVMs, Fourier, and Agnostic Learning.
    Other models of learning

    Jan 30. Online optimization. See paper by Zinkevich. For extension to the bandit setting, see this paper.
    Feb 6. Learning in repeated games. See Regret in the On-line Decision Problem by Foster and Vohra for a great introduction. To see the specific reduction from external to internal regret I was talking about, see From External to Internal Regret by Avrim Blum and Yishay Mansour. يمكنكم استخدام الروابط مع التحية

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  4. [14]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
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    Received: 7
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    Sleep and The Traveller Machine Gun Theory



    function writeTop(){ document.write('');document.write('');var channels = new String();if (!document.phpAds_used) document.phpAds_used = ',';phpAds_random = new String (Math.random()); phpAds_random = phpAds_random.substring(2,11);document.write ("");document.write('');document.write('<a href="http://campaign.indieclick.com/adclick.php?n=a9d29e46" target="_blank">');FastInit.addOnLoad(function(event){ if($('LastAd_Top')) {Element.show('LastAd_Top');}});FastInit.addOnLoad (function(event){ if($('catPage')) {Element.addClassName('catPage', 'underAds');}});};function writeTopRight(){};function writeTopTopRight(){};function writeTopTopTopRight(){};function writeMid(){ google_ad_client = "pub-8364350995539875";google_ad_width = 728;google_ad_height = 90;google_ad_format = "728x90_as";google_ad_type = "text_image";//2007-02-02: catalogue-overviewgoogle_ad_channel = "4073027658";google_color_border = "FFFFFF";google_color_bg = "FFFFFF"; google_color_link = "D01F3C";google_color_text = "605C5B";google_color_url = "605C5B";document.write("");FastInit.addOnLoad(fun ction(event){ if($('LastAd_Mid')) {Element.show('LastAd_Mid');}});}; writeTop();

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    Machine Gun Theory


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  5. [15]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
    Thumbs Up
    Received: 7
    Given: 0

  6. [16]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
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    A Glossary for Molecular Information Theory and the Delila System

    by Tom Schneider and Karen Lewis

    http://www.ccrnp.ncifcrf.gov/~toms/glossary.html
    Molecular Information Theory Glossary Terms (separate window)
    Molecular Information Theory Glossary Terms with a control frame on the left!
    Glossary without frames.
    Suggestions for new terms are welcome!


    absolute coordinate: A number (usually integer) that describes a specific position on a nucleic acid or protein sequence. An example of using two absolute coordinates in Delila instructions is: "get from 1 to 6;" The numerals 1 and 6 are absolute coordinates. See also: relative coordinate.
    acceptor splice site: The binding site of the spliceosome on the 3' side of an intron and the 5' side of an exon. This term is preferred over "3' site" because there can be multiple acceptor sites, in which case "3' site" is ambiguous. Also, one would have to refer to the 3' site on the 5' side of an exon, which is confusing. Mechanistically, an acceptor site defines the beginning of the exon, not the other way around. See
    acronymology: The study of words (as radar, snafu) formed from the initial letter or letters of each of the successive parts or major parts of a compound term. See also: acronymology example.
    administrivia: [Pronunciation: combine administ[ration] and trivia. Function: noun. Etymology: coined by TD Schneider. Date: before 2000] administritrative trivia
    after state (after sphere, after): the low energy state of a molecular machine after it has made a choice while dissipating energy. This corresponds to the state of a receiver in a communications system after it has selected a symbol from the incoming message while dissipating the energy of the message symbol. The state can be represented as a sphere in a high dimensional space. See also: Shannon sphere, gumball machine, channel capacity.
    alignment (align): a set of binding site or protein sequences can be brought into register so that a biological feature of interest is emphasized. A good criterion for finding an alignment is to maximize the information ******* of the set. This can be done for nucleic acid sequences by using the malign program. See also:
    before state (before sphere, before): the high energy state of a molecular machine before it makes a choice. This corresponds to the state of a receiver in a communications system before it has selected a symbol from the incoming message. The state can be represented as a sphere in a high dimensional space. See also: Shannon sphere, gumball machine, channel capacity.
    binding site: the place on a molecule that a recognizer (protein or macromolecular complex) binds. In this glossary, we will usually consider nucleic acid binding sites. A classic example is the set of binding sites for the bacteriophage Lambda Repressor (cI) protein on DNA (M. Ptashne, How eukaryotic transcriptional activators work, Nature, 335, 683-689, 1988). These happen to be the same as the binding sites for the Lambda cro protein. (The text mentioned in the figure is Sequence Logos: A Powerful Yet Simple, Tool.) See also

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  7. [17]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
    Thumbs Up
    Received: 7
    Given: 0

  8. [18]
    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


    تاريخ التسجيل: Nov 2006
    المشاركات: 1,338
    Thumbs Up
    Received: 7
    Given: 0
    تحياتي اخوكم حسن هادي

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