Institute of Formal and Applied Linguistics
Charles University, Czech Republic
Faculty of Mathematics and Physics
Úvod do strojového učení
Introduction to machine learning
Time and place
Lecture
- Czech – Wed, 10:40 – 12:10, S4
- English – Thu, 9:00 – 10:30, S3
Lab session
-
Czech – Mon, 17:20 – 18:80, SU2
Thu, 12:20 – 13:50, SU2 - English – Fri, 9:00 – 10:30, SU2
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: 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 2019/20
| Lecture date | Lecture | Lab | |
| 1. | 2–3/10 |
Introduction to Machine Learning – What is Machine Learning – Basic formal concepts – Entropy, its meaning and definition – Overview of the course – Requirements for getting credits |
Annotation experiment and data analysis
A gentle tutorial on elementary data analysis in R |
| 2. | 9–10/10 |
Data analysis – Basic data exploration – Association between attributes – K-Means clustering – Hierarchical agglomerative clustering |
R script
|
| 3. | 16–17/10 |
Tutorial on probability distributions and entropy in R Hints on computing entropy in R Tutorial on Decision Trees |
|
| 4. | 23–24/10 |
Linear regression, Logistic regression – Regression and classification – Least square method – Gradient Descent Algorithm – Sigmoid function |
R script
Fri 25/10 |
| 5. | 30–31/10 |
Lecture #5 – Details on learning Decision Trees – Decision Trees and overfitting – More about evaluation heuristics – Use of statistical tests will be discussed next time |
Mon 28/10 lab canceled (State holiday)
Wed 30/10 |
| 6. | 6–7/11 |
More supervised learning algorithms – Instance-based learning – Naive Bayes classifier – Bayesian networks |
|
| 7. | 13–14/11 | Ensemble learning methods |
Wed 13/11 – HW #2 early submission date |
| 8. | 20–21/11 |
Wed 20/11 Thu 21/11 lecture canceled (Open Door Day) |
Wed 20/11 Thu 21/11 lab canceled (Open Door Day)
Fri 22/11 |
| 9. | 27–28/11 | The curse of dimensionality and feature selection | |
| 10. | 4–5/12 | Support Vector Machines | |
| 11. | 11–12/12 | Regularization, PCA | |
| 12. | 18–19/12 | Fundamentals of Neural Networks | |
| 13. | 8–9/1 |
Wed 8/1 and Thu 9/1 – Obligatory final written test in the lecture time |
Mon 6/1 – HW #3 hard deadline |
|
|
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 Machines 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
Other machine learning courses organized by UFAL
- NPFL097 Selected problems in machine learning
- NPFL104 Machine learning exercises
- NPFL114 Deep learning