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

• The most important requirements from probability and statistics are listed here: Preliminaries.Probability-Statistics
• Make sure that you are familiar at least with the very basics: Prob-Stat.zaklady.2014
• As to the MFF students, we expect the knowledge covered in the obligatory course "Pravděpodobnost a statistika" (NMAI059).
• Gentle entry test in probability and statistics – a brief evaluation: Mar 2021, Oct 2019, Oct 2018

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. (link, a 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)

MFF UK Internal Regulations

• Vnitřní předpisy (CZ)
• Studijní předpisy (CZ)