machine learning textbook introduction to machine learning (ethem alpaydin).htm

Machine Learning with WEKA: An Introduction （讲义） 关于数据挖掘和机器学习的.

    Parker CS 494/595 (Spring 2006) U Tennessee (US)</A> 
    <LI><A href="http://www.cs.nmsu.edu/~ipivkina/cs579/syllabus.html">I Pivkina 
    CS 479/CS 579 (Spring 2005) New Mexico State U (US)</A> 
    <LI><A 
    href="http://povinelli.eece.mu.edu/teaching/eece229ml/syllabus.html">R 
    Povinelli EECE 229 (Spring 2005) Marquette U (US)</A> 
    <LI><A href="http://www.cs.mcgill.ca/~dprecup/courses/ML/home.html">D Precup 
    Comp-652 (Fall 2005) McGill (CA)</A> 
    <LI><A 
    href="http://www.cs.manchester.ac.uk/Study_subweb/Postgrad/ACS-CS/webpages/syllabus/acs/CS643.php">M 
    Rattray CS643 (2005) UManchester (UK)</A> 
    <LI><A 
    href="http://www.facweb.iitkgp.ernet.in/~sudeshna/courses/ML06/index.html">S 
    Sarkar CS60050 (Spring 2006) IIT Kharagpur (IN)</A> 
    <LI><A href="http://pami.uwaterloo.ca/tizhoosh/sd422.htm">H Tizhoosh SYDE 
    422 (Winter 2004) U Waterloo (CA)</A> 
    <LI><A href="http://www.svcl.ucsd.edu/courses/ece175/">N Vasconcelos ECE175 
    (Spring 2006) UCSD (US)</A> 
    <LI><A 
    href="http://www2.cs.uh.edu/~vilalta/courses/machinelearning/machinelearning.html">R 
    Vilalta COSC 6342 (Fall 2006) U Houston (US)</A> 
    <LI><A href="http://people.sabanciuniv.edu/~berrin/cs512/">B Yanikoglu CS 
    512 (Spring 2006) Sabanci U (TR)</A> 
    <LI><A href="http://www3.stat.sinica.edu.tw/stat2005w/schedule.htm">(2005) 
    Nat TW Univ of Sci and Tech (TW)</A> 
    <LI><A 
    href="http://www.itee.uq.edu.au/~comp4702/material.html">COMP4702/COMP7703 
    (Spring 2006) U Queensland (AU)</A> </LI></UL>
  <LI><B>Reference book:</B> 
  <UL>
    <LI><A href="http://zoo.cs.yale.edu/classes/cs463/2005/syllabus.html">D 
    Angluin 463a (Fall 2005) U Yale (US)</A> 
    <LI><A href="http://www.ceng.metu.edu.tr/courses/ceng574/">V Atalay CENG 574 
    (Spring 2006) Middle East Tech U (TR)</A> 
    <LI><A href="http://www.cs.mu.oz.au/680/lectures/week01a-8up.pdf">T Baldwin 
    433-680 (Spring 2005) U Melbourne (AU)</A> 
    <LI><A 
    href="http://www.dc.fi.udc.es/tercerciclo/VerCursoDetalleAction.do?elIdCurso=49">AA 
    Betansoz, OF Romero, MFG Penedo, BG Berdinas, EM Rey, JS Reyes, CV Martin 
    <I>Aprendizaje Maquina</I> (Spring 2006) U da Coruna (ES)</A> 
    <LI><A href="http://di002.edv.uniovi.es/~alguero/eaac/eaac.html">J Brugos, A 
    Alguero 383 (2005) U Oviedo (ES)</A> 
    <LI><A href="http://www.csie.ncu.edu.tw/~chia/Course/DM/">C-H Chang (2006) 
    Nat Central U (TW)</A> 
    <LI><A href="http://www.iro.umontreal.ca/~pift6080/">D Eck IFT 6080 (2005, 
    2006) U Montreal (CA)</A> 
    <LI><A href="http://www.cs.umd.edu/class/spring2004/cmsc726/">L Getoor CMSC 
    726 (Spring 2004) U Maryland (US)</A> 
    <LI><A href="http://www.cs.auc.dk/~jaeger/DAT5/mi2-2005.htm">M Jaeger 
    Dat5/F9D/KDE3 (Fall 2005) Aalborg U (DK)</A> 
    <LI><A href="http://www.cs.cornell.edu/Courses/cs478/2006sp/">T Joachims 
    CS478 (Spring 2006) Cornell U (US)</A> 
    <LI><A href="http://suraj.lums.edu.pk/~cs535w05/">A Karim CS/CMPE 535 
    (Winter 2005) Lahore U of Management Sciences (PK)</A> 
    <LI><A href="http://www-scf.usc.edu/~csci567/">S A Macskassy CSCI 567 
    (Spring 2006) U Southern California (US)</A> 
    <LI><A 
    href="http://www.frsf.utn.edu.ar/estudios_y_acceso/posgrado/maestria-isi/cursos/2006/SistIntel.htm">E 
    Martinez (Spring 2006) UTN Santa Fe (AR)</A> 
    <LI><A href="http://www.fdaw.unimaas.nl/education/M.1ML/info.htm">E O Postma 
    Machine Learning (Fall 2006) U Maastricht (NL)</A> 
    <LI><A href="http://www.isys.ucl.ac.be/etudes/cours/linf2275/support.htm">M 
    Saarens LINF 275 (Spring 2004) UC Louvain (BE)</A> 
    <LI><A href="http://www.ee.technion.ac.il/courses/046195/">N Shimkin (Spring 
    2005) Israel Inst of Tech (Technion) (IL)</A> 
    <LI><A href="http://www.liacs.nl/~kosters/AI/">W Walter (Spring 2005) U 
    Leiden (NL)</A> </LI></UL></LI></UL><A name=figs><B>Figures:</B></A> The 
complete set of figures can be retrieved as a <A 
href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/i2ml-figs.pdf">pdf file</A> (2 
MB). Instructors using the book are welcome to use these figures in their 
lecture slides as long as the use is non-commercial and the source is cited. 
<P><A name=lecs><B>Lecture Slides:</B></A> The following lecture slides (pdf and 
ppt) are made available for instructors using the book. 
<P>
<UL>
  <LI>Chapter 1. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap1-v1-1.pdf">Introduction</A> 
  <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap1-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 2. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap2-v1-1.pdf">Supervised 
  Learning</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap2-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 3. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap3-v1-1.pdf">Bayesian 
  Decision Theory</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap3-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 4. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap4-v1-1.pdf">Parametric 
  Methods</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap4-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 5. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap5-v1-1.pdf">Multivariate 
  Methods</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap5-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 6. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap6-v1-1.pdf">Dimensionality 
  Reduction</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap6-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 7. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap7-v1-1.pdf">Clustering</A> 
  <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap7-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 8. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap8-v1-1.pdf">Nonparametric 
  Methods</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap8-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 9. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap9-v1-1.pdf">Decision 
  Trees</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap9-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 10. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap10-v1-1.pdf">Linear 
  Discrimination</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap10-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 11. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap11-v1-1.pdf">Multilayer 
  Perceptrons</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap11-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 12. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap12-v1-1.pdf">Local 
  Models</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap12-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 13. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap13-v1-1.pdf">Hidden 
  Markov Models</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap13-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 14. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap14-v1-1.pdf">Assessing 
  and Comparing Classification Algorithms</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap14-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 15. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap15-v1-1.pdf">Combining 
  Multiple Learners</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap15-v1-1.ppt">(ppt)</A> 

  <LI>Chapter 16. <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap16-v1-1.pdf">Reinforcement 
  Learning</A> <A 
  href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap16-v1-1.ppt">(ppt)</A> 
  </LI></UL>
<P><A name=err><B>Errata:</B></A> Download errata as a <A 
href="http://www.cmpe.boun.edu.tr/~ethem/i2ml/errata.pdf">pdf file</A>. 
<UL>
  <LI>(p. 20-22): S and G need not be unique. (Luc de Raedt) 
  <P>Depending on the training set and the hypothesis class, there may be 
  several S_i and G_j which respectively make up the S-set and the G-set. Every 
  member of the S-set is consistent with all the instances and there are no 
  consistent hypotheses that are more specific. Similarly, every member of the 
  G-set is consistent with all the instances and there are no consistent 
  hypotheses that are more general. These two make up the boundary sets and any 
  hypothesis between them is consistent and is part of the version space. There 
  is an algorithm called candidate elimination that incrementally updates the S- 
  and G-sets as it sees training instances one by one. See (Mitchell, 1997; 
  Russell and Norvig; 1995). </P>
  <LI>(p. 30): Eq. 2.15: w_1 x + w_0 should be w_1 x^t + w_0 (Mike Colagrosso) 
  <LI>(p. 30): Eq. 2.15: Not needed, but the summation should be multiplied by 
  1/N to match Eq. 2.12 (Mike Colagrosso) 
  <LI>(p. 35): Eq. 2.19: Missing closing ')' (Mike Colagrosso) 
  <LI>(p. 63): Eq. 4.5: p(x_1, x_2, \dots, x_K) should be P(x_1, x_2, \dots, 
  x_K). That is, P should be uppercase. (Mike Colagrosso) 
  <LI>(p. 86): Eq. 5.3: '[' missing after the first 'E'. (Hakan Haberdar) 
  <LI>(p. 90): Figure 5.2: Upper-left figure should be a circle, but the plot is 
  squashed. x_1 axis is longer than the x_2 axis. (Mike Colagrosso) 
  <LI>(p. 191): Figure 9.8: w_{11} x_1 + w_{12} x_2 + w_{10} = 0 should be 
  w_{11} x_1 + w_{12} x_2 + w_{10} &gt; 0 (Mike Colagrosso) 
  <LI>(p. 203): Eq. 10.7: w_{i0} shouldn't be bold. It's a scalar, not a vector, 
  as in the sentence above and Eq. 10.6. (Mike Colagrosso) 
  <LI>(p. 209): Eq. 10.23: E(w, w_o | X) should be E(w, w_0 | X). That is, the 
  subscript should be a zero, not an "oh." (Mike Colagrosso) 
  <LI>(p. 227): First sentence of 10.10: Change "discriminant" to 
  "discrimination" (Mike Colagrosso) 
  <LI>(p. 227): Exercise 1: change "function" to "functions" (Mike Colagrosso) 
  <LI>(p. 238): In the first cross-entropy eq on the top of the page, the 
  summation over i and all i subscripts should be omitted. (David Warde-Farley) 
  <LI>(p. 239): First word in the Figure 11.3 narrative should be "Perceptron" 
  instead of "Percepton." (Mike Colagrosso) 
  <LI>(p. 252): sigmoid() missing in the second terms to the right of eqs 
  defining z_1h and z_2l. 
  <LI>(p. 257): Insert "is" before "as" in the last sentence of the first 
  paragraph to read "..., it is as if the training set ..." (Tunga Gungor) 
  <LI>(p. 267): Fig. 11.20: The input units $x^{t-\tau},...,x^{t-1},x^t$ should 
  be labeled in the opposite order; or equivalently, the arrows should point to 
  the left. $x^t$ is the current input seen (the latest) and $x^{t-\tau}$ is the 
  input seen $\tau$ steps in the past (delayed $\tau$ times). 
  <LI>(p. 288): Remove the extra "the" in the first sentence. (Tunga Gungor) 
  <LI>(p.317): Fig. 13.4: Below the node for state j, '1' should follow the line 
  $O_{t+}$; that is, the observation is named $O_{t+1}$. 
  <LI>(p.319): Eq. 13.32: In estimating b_j(m), t should range from 1 to T_k 
  (and not T_k-1) in both the numerator and the denominator. (Cem Keskin) 
  <LI>(p. 320): Eq. 13.35: Drop j in P(G_{jl}). (Cem Keskin) 
  <LI>(p. 330): "than" on line 16 should be changed to "then." (Tunga Gungor) 
  <LI>(p. 375): First paragraph of 16.2: classification is misspelled. (Mike 
  Colagrosso) </LI></UL>
<HR>
<I>Created on Oct 24, 2004 by E. Alpaydin (my_last_name AT boun DOT edu DOT tr)
<P>
<UL>
  <LI>Jan 14, 2005: Added links to more online booksellers. 
  <LI>Jan 31, 2005: Added link to the pdf file of figures. 
  <LI>Apr, 3, 2005: Added Errata. 
  <LI>June 1, 2005: Further errata. 
  <LI>July 12, 2005: Added more bookseller link. 
  <LI>July 20, 2005: Added more bookseller links and the lecture slides of 
  Chapters 1, 2 and 11. 
  <LI>July 28, 2005: Added all lecture slides. 
  <LI>Sep 26, 2005: Added ppt of all lecture slides. This new version (V1-1) is 
  the same as the previously available V1-0 except that I retyped all equations 
  using Microsoft Equation Editor. 
  <LI>Oct 25, 2005: Further errata. 
  <LI>Nov 12, 2005: Added reviews and courses. 
  <LI>Dec 14, 2005: Added links to MIT Press for sample pdfs of Foreword, 
  Preface, and Chapter 1. 
  <LI>Feb 1, 2006: Added links to 2006 courses. 
  <LI>Apr 27, 2006: Added new course links and errata. 
  <LI>Jul 4, 2006: Added errata.pdf 
  <LI>Sep 1, 2006: Added links to Fall 2006 courses. </I></LI></UL></BODY></HTML>