Institute of Formal and Applied Linguistics
Charles University, Czech Republic
Faculty of Mathematics and Physics
SIS code:
Semester:
winter
E-credits:
winter s.:6
Examination:
2/2 C+Ex
Guarantor:
Úvod do strojového učení
Introduction to machine learning
Time and place
Lecture
- Czech – Wednesday 10:40 - 12:10, S1
- English – Wednesday 12:20 - 13:50, S9
Lab session
- Czech – Friday 12:20 - 13:50, SU2
- English – Friday 14:00 - 15:30, SU2
Math and programming requirements
Probability and statistics
- Make sure that you are familiar at least with the very basics: Prob-Stat.zaklady.2014
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.
As to the MFF students, we expect the knowledge covered in the obligatory course "Pravděpodobnost a statistika" (NMAI059).
Calendar 2017/18
| Date | Lecture (Wednesdays) | Lab (Fridays) | |
| 1. | 4/10 6/10 |
Introduction to Machine Learning – What is Machine Learning – Basic formal concepts – Overview of the course |
Annotation experiment and data analysis – Practical experience with manual annotation – Elementary data processing in R – Annotation data analysis – Inter-annotator agreement – Confusion matrices and error analysis Tutorial on annotation data analysis in R |
| 2. | 11/10 13/10 |
Entropy and Decision Trees Note: The part about DT will be updated. Here is only a tentative version of whole presentation. I am going to simplify it a bit...) |
Exercises on entropy and remarks on evaluation Note on homeworks: All homeworks so far are strongly recommended and you do NOT have to submit them. Tutorial on probability distributions and entropy in R – Data: xy.100.csv Demo codes – load-wsd-data.R – forbes.data-preparation.R – cp-and-pruning.Forbes.R |
| 3. | 18/10 20/10 |
Data analysis and Clustering |
Demo code for data analysis and clustering – ml-lab.2017-10.20.R – HW #1 assignment |
| 4. | 25/10 27/10 |
Evaluation and statistical tests Note: Using t-test will be practiced together on Nov 10 at the Lab session. Chi-square tests were postponed and will be explained only at some of the next Lectures. |
Practical exercises in R -- Decision Trees Tutorial on Decision Trees |
| 5. | 1/11 3/11 |
Demo code for linear and logistic regression Written test #1 (30 minutes) |
|
| 6. | 10/11 |
Dean's Day (no lecture) |
Practical exercises in R -- Statistical tests Data: test1.anonymous.csv |
| 7. | 15/11 |
Instance-based learning, Naive Bayes Classfiers, Bayesian networks – presentation of the HW #1 solution – HW #2 assignment |
State Holiday (no lab session) |
| 8. | 22/11 24/11 |
Ensemble learning |
Demo code for kNN, Naive Bayes |
| 9. | 29/11 1/12 |
Support Vector Machines, ROC curve | Native Language Identification Task and SVM – ml-lab.2017-12-01.pdf – ml-lab.SVM.NLI.2017-12.01.R Demo code for ROC curve and AUC – ml-lab.ROC.2017-12.01.R HW #2 early due date 1/12 |
| 10. | 6/12 8/12 |
Chi-square tests Recommended materials
|
HW #2 late due date 8/12 |
| 11. | 13/12 15/12 |
Regularization, Principal Component Analysis |
Demo code for |
| 12. | 20/12 22/12 |
Foundations of Neural Networks |
Learning ensemble models |
| 13. | 3/1 5/1 |
Machine Learning for Automatic Machine Translation |
Maximum Likelihood Estimation Summarizing remarks |
| 14. | 10/1 12/1 |
Obligatory session: Final Written Test #3 (80 minutes) – on Jan 10 (Wednesday) in the regular lecture time |
Concluding lab lesson – example solution of HW #3 – correct solution of Test #3 |
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]
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