esslli2015

Visit the ESSLLI 2015 site

Dear students,

we remember a great week that we spent together in Barcelona! We have just posted the final versions of our teaching materials. Feel free to download them and study them.

All the best, Barbora & Martin

September 2, 2015

 

	Day 1	Day 2	Day 3	Day 4	Day 5
slides	Day1.pdf	Day2.pdf	Day3.pdf	Day4.pdf	Day5.pdf
illustrations		plot.scripts.zip	vpr.ensembles		feature-selection.lasso.cry     vpr.feature-selection     vpr.summary.table
R scripts		Homeworks:     hw-1.2-1.R     hw-1.2-2.R     hw-1.2-3.R	Demo codes:     vpr.functions.R     dt.R     bagging.R     rf.R     ab.R     svm.R    Homework:     hw-2.1-12.R	Demo codes - Regression:     lin-reg-mov-cv.R     lin-reg-ridge-mov-cv-glmnet.R     lin-reg-lasso-mov-cv-glment.R     log-reg-cry-cv-manually.R     log-reg-ridge-cry-cv-glmnet.R     log-reg-lasso-cry-cv-glmnet.R    Demo codes - SVM:     svm.tuning.cost.R     svm.tuning.fr.R     svm.fs-lasso.cry.R	Demo codes:    var-imp.ab.R    boostrap-vpr-cry-bagging.R

Tasks

• Movie Recommendation
  • mov.data.zip
  • mov.handout
  • R-code to load the data into R
• ​Verb Pattern Recognition
  • ​vpr.data.zip
  • vpr.handout
  • For better understanding the VPR task fill the quiz!
    • quiz online
    • quiz.pdf
  • Semantic Pattern Recognition web page
  • Materials with details about the feature set
    • Semantic prototype tags for nouns -- description by Eckhard Bick (2009) link
    • semantic-features.definition.pdf
    • Stanford dependencies manual link

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. (link)
• 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. (link)
• Gonick, Larry and Woollcott Smith. The Cartoon Guide to Statistics. Harper Resource. 2005.
• Hladká Barbora, Holub Martin: A Gentle Introduction to Machine Learning for Natural Language Processing: How to start in 16 practical steps.In: Language and Linguistics Compass, vol. 9, No. 2, pp. 55-76, 2015.
• Hladká Barbora, Holub Martin: Machine Learning in Natural Language Processing using R. Course at ESSLLI2013, 2013.
• Kononenko, Igor and Matjaz Kukar. Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing, 2007. (link, a light survey of the whole field)
• Lantz, Brett. Machine learning with R. Packt Publishing Ltd. 2013.

More advanced textbooks and tutorials (deeper mathematical background)

• Baayen, R. Harald. Analyzing Linguistic Data: A Practical Introduction to Statistics using R. Cambridge University Press, 2008.
• Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006.
• Burges Christopher J. C.  A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998. (link)
• 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.
• Guyon, Isabelle and Gunn, Steve and Nikravesh, Masoud and Zadeh, Lotfi A. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag New York, Inc. 2006.
• Hastie, Trevor, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009. (link)
• Hsu Chih-Wei, Chang Chih-Chung Chang and Chih-Jen Lin. A Practical Guide to Support Vector Classication. 2010. (link)
• James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert. An Introduction to Statistical Learning. Springer New York, 2013. (link)

About R

• Everitt, B.S and Hothorn, Torsten. A Handbook of Statistical Analyses using R. CRC Press. 2010.
• Dalgaard, Peter. Introductory Statistics with R. Springer, 2008.
• Kerns, G. Jay. Introduction to Probability and Statistics Using R. 2011. (link) ​
• Paradis, Emmanuel. R for Beginners. 2005. (link)
• Rodrigue, German. Introducing R -- Getting started. (link)
• Venables, W.N, D. M. Smith and the R core team. An Introduction to R. (link)
• Venables, W. N. and B. D. Ripley. Modern Applied Statistics with S. Springer, 2002. (link)

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, grant no. P103/12/G084 and Charles University in Prague, Faculty of Mathematics and Physics.