SIS code: 
Semester: 
summer
E-credits: 
5
Examination: 
2/2 C+Ex

Úvod do strojového učení v systému R

Introduction to machine learning in the R system

  • Czech lecture     Wednesday 12:20-13:50, S11
  • Czech lab            Friday 12:20-13:50, SW2
  • English lecture   Wednesday 14:00-15:30, S1
  • English lab          Friday 14:00-15:30, SW2

 

Math and programming requirements

Probability and statistics

R programming

  • You can start with a simple tutorial Tutorial-on-R.2013
  • If you are not familiar with elementary R functions, use the resources listed below.

Calendar

  Lecture date Lecture Lab
1. Feb 16 Introductory lesson
(will be finished next time)

Annotation experiment – Demo

Working with R – Tutorial on annotation data analysis
(only Parts I and II today)
 

2. Feb 23 Data analysis, Clustering Programming questions
ml-lab.2022-02.25.R
3. Mar 2

Decision Trees – basic structure

Data, evaluation, and overfitting

Inter-annotator agreement – Cohen's kappa

Tutorial on Decision Trees – simple exercise in R
(only Parts I and II today)

4.  Mar 9 Linear regression Programming questions
-
 
ml-lab.2022-03-11.R
5. Mar 16 lecture cancelled lab session cancelled
6.  Mar 23 Logistic regression, ​evaluation of binary classifiers Programming questions
-
 
ml-lab.2022-03-25.R
7. Mar 30 Learning Decision Trees & Random Forests
    – Entropy
    – Learning algorithms
       (Random Forests will be explained next time)

 
Decision Trees, evaluation, and overfitting
    Example code + illustrations
8. Apr 6 Support Vector Machines, Naive Bayes classifier

Programming questions

HW #1 assignment

9. Apr 13

Test #1     –  obligatory 

Ensemble learning – bagging and boosting
    – simple bagging
    – Random Forests
    – boosting regression trees
    – AdaBoost

No lab session (Easter)
10. Apr 20

HW #1 due date

Regularization

ml-lab2022-04.22.R

HW #2 assignment

11. Apr 27 Statistical tests  –  applications in ML
    – general principles
    – t-test and its use
    – example code

Exercise on t-test
    – paired t-test: see the example attached to the lecture

Pearson's chi-squared tests 
    – Example on the goodness-of-fit test
    – Tutorial on the independence test (data: xy.100)

12. May 4 Principa Component Analysis, Maximum Likelihood Estimation ml-lab.PCA.2022-05.06.R
ml-lab.SVM.2022-05.06.R
13.  May 11 No lecture (Rector's Day) Students' presentations
Discussion on HW #2
14. May 18

Remarks on Bayes classifier and Bayes error

Fundamentals of Neural Networks
    – single perceptron learning
    – multi-layer perceptron
    – historical remarks

– HW #2 due date

Students' presentations

Discussion on HW #2
     – implemented models and results

Prepare for Test #2

  May 31, 9:00 Exam, room S1  
  June 8, 9:00 Exam, room S10  
  June 15, 9:00 Exam, room S1  

Literature

Recommended readings

  • James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert. An Introduction to Statistical Learning. Springer New York, 2013. (link
  • Lantz, Brett. Machine learning with R. Packt Publishing Ltd. 2013. [available  in the MFF library]
  • Barbora Hladká — Martin Holub — Vilém Zouhar: A Collection of Machine Learning Excercises

Introductory readings

  • 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. (linka light survey of the whole field)

Advanced readings

  • 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)

About the R system

  • 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)

Sample student projects from the past

This course was originally focused on machine learning in natural language processing. To get credits for lab sessions, students needed to do experimental projects

  Default Task Default Task Description Nice Student Reports
2014/15 Native Language Identification npfl054-term-project-2014-15.pdf
CUNI report
 
2013/14

Reuters-21578 Text Categorization

text-categorization.pdf
test-collection.README.txt
3-classes.distribution.pdf
Default task:
    Luksova.report.final.2013-14.pdf
Sentiment analysis task:
    Tam.report.final.2013-14.pdf
2012/13 Word Sense Disambiguation PFL054.project.2012-13.pdf Barancikova.report.final.2012-13.pdf
Machacek.report.final.2012-13.pdf
Franky.report.final.2012-13.pdf
2011/12 Semantic Pattern Classification PFL054.project.2011-12.specification.pdf Krejcova.report.final.2011-12.pdf
Long.report.final.2011-12.pdf
Tamchyna.report.final.2011-12.pdf
2010/11 Semantic Collocation Recognition PFL054.project.2010-11.pdf,
features.description.pdf
Lauschmannova.report.final.2010-11.pdf
Hajic.report.updated.2010-11.pdf
Kriz.report.final.2010-11.pdf
2009/10 Verb Sense Disambiguation PFL054_2009_10_project.pdf ML_report_Fabian.pdf,
ML_report_Galuscakova.pdf,
ML_report_Larasati.pdf
2008/09 Coreference Resolution

PFL054_2008_09_project.pdf

ML_report_Dusek.pdf,
ML_report_LeThanhDinh.pdf,
ML_report_Novak.pdf
2007/08 Named-entity Type Classification PFL054_2007_08_project.pdf Jana.Kravalova-FinalReport.pdf,
Sergio.Duante-finalReport.pdf,
Zorana.Ratkovic-Final_Report.pdf

 

Other machine learning courses organized by UFAL

  • NPFL097 Unsupervised Machine Learning in NLP (advanced)
  • NPFL114 Deep learning (introductory)
  • NPFL122 Deep Reinforcement Learning (advanced)
  • NPFL129 Introduction to Machine Learning with Python (introductory)

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