esslli 2013

Visit the ESSLLI 2013 site.​​

Dear students,

our course is over. We really enjoyed a great week with you on the way to machine learning! We've just posted the final versions of our presentations. Feel free to download them and study them. 

All the best, Barbora & Martin

August 27, 2013

 

	Day 1	Day 2	Day 3	Day 4	Day 5
slides	day-1.posted.pdf	day-2.posted.pdf	day-3.posted.pdf	day-4.posted.pdf	day-5.posted.pdf
HW	homework-1.1-solution.posted.ods	homework-2.2-solution.pdf	hw-3.1-solution.pdf, HW-3.1-examples.in.R	hw-4.1-solution.pdf, HW-4.1.R
R scripts		DT-WSD.R	NB-WSD.R	SVM-COL.R, entropy.R	do-cv.R, load-col-data.R

Tasks

• Word Sense Disambiguation
  • Data -- wsd.test.csv, wsd.development.csv (see Leacock et al., 1993 and original README file)
  • Handout --  wsd.pdf
• ​Semantic Collocations Identification
  • ​Data -- col.test.csv, col.development.csv  (see Pecina, 2008)
  • Handout -- col.pdf

R system

• home page of R http://www.r-project.org/
• R reference card  http://cran.r-project.org/doc/contrib/Short-refcard.pdf

References

Introductory reading

• Alpaydin, Ethem. Introduction to Machine Learning. The MIT Press. 2004, 2010 (url).

• Domingos, Pedro. A few useful things to know about Machine learning. Communication of the ACM, vol. 55, Issue 10, October 2012, pp. 78--87, ACM, New York, USA. (pdf) [a nice non-technical reading]

• Gonick, Larry and Woollcott Smith. The Cartoon Guide to Statistics. Harper Resource. 2005. 

• Hladka, Barbora and Martin Holub. The course proposal esslli-proposal.2013.pdf, 2013.

• Kononenko, Igor and Matjaz Kukar. Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing, 2007 (url). [a light survey of the whole field]

Serious textbooks and tutorials (they require deeper mathematical background)

• Bishop, Christopher M. Pattern Recognition And Machine Learning. Springer, 2006 (url).

• Burges Christopher J. C.  A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998. http://research.microsoft.com/pubs/67119/svmtutorial.pdf

• Cristianni, Nello and John Shawe-Taylor. An Introduction to Support Vector M​achines and other Kernel-based Learning Methods. Cambridge University Press, 2000.

• Duda, Richard O., Peter R. Hart and David G. Stork. Pattern Classification. Second Edition. Wiley, 2001.

• Hsu Chih-Wei, Chang Chih-Chung Chang and Chih-Jen Lin. A Practical Guide to Support Vector Classication. 2010. (pdf).

• Hastie, Trevor, Robert Tibshirani and Jerome Friedman.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009 (url).

About R

• Baayen, R. Harald: Analyzing Linguistic Data: A Practical Introduction to Statistics using R. Cambridge University Press, 2008 (url).[focused on NLP applications]

• Dalgaard, Peter: Introductory Statistics with R. Springer, 2008.

• Everitt, Brian S. and Torsten Hothorn. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC, 2006 (url). ​

• Kerns, G. Jay. Introduction to Probability and Statistics Using R. 2011.​​

• Paradis, Emmanuel. R for Beginners. 2005. (http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf)

• Rodrigue, German. Introducing R -- Getting started (http://data.princeton.edu/R/gettingStarted.html).

• Venables, W. N. and B. D. Ripley. Modern Applied Statistics with S. Springer, 2002 (url).

• Venables, W.N, D. M. Smith and the R core team. An Introduction to R. (http://cran.r-project.org/doc/manuals/R-intro.html)

​​About data used

• Leacock, C., Towell, G. and Voorhees, E. Corpus-Based Statistical Sense Resolution. In Proceedings of the ARPA Workshop on Human  Language Technology, pp. 260--265. 1993. [WSD task]  ​
• Pecina, Pavel. Reference Data for Czech Collocation Extraction. In Proceedings of the LREC 2008 Workshop Towards a Shared Task for Multiword Expressions, pp. 11-14, Marrakech, Morocco. 2008. [COL task]

Contacts

Barbora Hladka and Martin Holub

Institute of Formal and Applied Linguistics

Faculty of Mathematics and Physics

Charles University in Prague

Acknowledgements

Teaching the course was supported by the Czech Science Foundation, grants no. P103/12/G084, P406/12/0658 and Charles University in Prague, Faculty of Mathematics and Physics.