PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2024
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
In-person direct teaching - Teacher(s)
- doc. Mgr. Bc. Vít Nováček, PhD (lecturer)
RNDr. Ondřej Sotolář (assistant) - Guaranteed by
- doc. Mgr. Bc. Vít Nováček, PhD
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 23. 9. to Mon 16. 12. Mon 14:00–15:50 C525
- Timetable of Seminar Groups:
- Prerequisites
- The basics of machine learning (e.g. IB031), computational linguistics (e.g. PA153) and neural networks (e.g. PV021), is assumed. The course is given in English (or in Czech depending on the audience). Task solutions can be in English, Czech or Slovak (exceptionally in another language).
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 29 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Course overview, a sample text (pre)processing pipeline
- Quick and dirty intro to ML
- Distributional semantics, LSA, word embeddings
- Deep neural networks for NLP
- Language models and their applications
- AutoML for NLP
- Student poster session(s), including extensive feedback during the students' work and its presentation
- Application example: sentiment analysis
- Application example: knowledge extraction from text
- Guest lecture(s) from international experts on various ML applications in the NLP field
- Final project presentations
- Literature
- recommended literature
- Charu C. Aggarwal, Machine Learning for Text. Springer 2018
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with independent work on and demonstrations of selected techniques in the labs, work on a project
- Assessment methods
- Oral examination with written preps (optional). Project presentations are a part of the examination.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually.
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2023
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. Mgr. Bc. Vít Nováček, PhD (lecturer)
- Guaranteed by
- doc. Mgr. Bc. Vít Nováček, PhD
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 14:00–15:50 A318
- Timetable of Seminar Groups:
- Prerequisites
- The basics of machine learning (e.g. IB031), computational linguistics (e.g. PA153) and neural networks (e.g. PV021), is assumed. The course is given in English (or in Czech depending on the audience). Task solutions can be in English, Czech or Slovak (exceptionally in another language).
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 55 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Course overview, a sample text (pre)processing pipeline
- Quick and dirty intro to ML
- Distributional semantics, LSA, word embeddings
- Deep neural networks for NLP
- Language models and their applications
- AutoML for NLP
- Student poster session(s), including extensive feedback during the students' work and its presentation
- Application example: sentiment analysis
- Application example: knowledge extraction from text
- Guest lecture(s) from international experts on various ML applications in the NLP field
- Final project presentations
- Literature
- recommended literature
- Chang, Yupeng, et al. "A survey on evaluation of large language models." ACM Transactions on Intelligent Systems and Technology 15.3 (2024): 1-45.
- Zhao, Wayne Xin, et al. "A survey of large language models." arXiv preprint arXiv:2303.18223 (2023).
- Charu C. Aggarwal, Machine Learning for Text. Springer 2018
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- Teaching methods
- a lecture combined with independent work on and demonstrations of selected techniques in the labs, work on a project
- Assessment methods
- Oral examination with written preps (optional). Project presentations are a part of the examination.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually.
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2022
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
doc. Mgr. Bc. Vít Nováček, PhD (lecturer)
RNDr. Ondřej Sotolář (assistant) - Guaranteed by
- doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 14:00–15:50 A217
- Timetable of Seminar Groups:
- Prerequisites
- The basics of machine learning (e.g. IB031), computational linguistics (e.g. PA153) and neural networks (e.g. PV021), is assumed. The course is given in English (or in Czech depending on the audience). Task solutions can be in English, Czech or Slovak (exceptionally in another language).
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 55 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Keyness. Keyword detection
- Anomaly detection in text. Novelty detection
- Document and term clustering
- Web mining
- Literature
- recommended literature
- Charu C. Aggarwal, Machine Learning for Text. Springer 2018
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2021
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
doc. Mgr. Bc. Vít Nováček, PhD (lecturer) - Guaranteed by
- doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Thu 16. 9. to Thu 9. 12. Thu 12:00–13:50 A318
- Timetable of Seminar Groups:
- Prerequisites
- The basics of machine learning (e.g. IB031), computational linguistics (e.g. PA153) and neural networks (e.g. PV021), is assumed. The course is given in English (or in Czech depending on the audience). Task solutions can be in English, Czech or Slovak (exceptionally in another language).
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 54 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Keyness. Keyword detection
- Anomaly detection in text. Novelty detection
- Document and term clustering
- Web mining
- Literature
- recommended literature
- Charu C. Aggarwal, Machine Learning for Text. Springer 2018
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2020
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Adam Bajger (assistant) - Guaranteed by
- doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 9:00–11:50 A318
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 54 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught annually. - Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2018
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Karel Vaculík, Ph.D. (assistant)
Mgr. Veronika Krejčířová (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Wed 8:00–10:50 C513
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 25 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2017
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Karel Vaculík, Ph.D. (assistant)
Mgr. Veronika Krejčířová (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Mon 9:00–11:50 C525
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 25 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2016
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Karel Vaculík, Ph.D. (assistant)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Jan Sedlák (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Wed 10:00–12:50 B411
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 25 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2015
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- RNDr. Karel Vaculík, Ph.D. (assistant)
RNDr. Hana Bydžovská, Ph.D. (assistant)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Tue 8:00–9:50 C416
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 25 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2014
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Hana Bydžovská, Ph.D. (assistant)
Mgr. Lukáš Másilko (assistant)
RNDr. Karel Vaculík, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Tue 8:00–9:50 C511
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 24 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- Learning language in logic. Edited by Sašo Džeroski - James Cussens. Berlin: Springer, 2000, x, 299. ISBN 3540411453. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2013
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Juraj Jurčo (assistant)
RNDr. Karel Vaculík, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Tue 12:00–13:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 24 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- Learning language in logic. Edited by Sašo Džeroski - James Cussens. Berlin: Springer, 2000, x, 299. ISBN 3540411453. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2012
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Juraj Jurčo (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Wed 10:00–11:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 24 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- Learning language in logic. Edited by Sašo Džeroski - James Cussens. Berlin: Springer, 2000, x, 299. ISBN 3540411453. info
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2011
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- Mgr. Jan Knotek (assistant)
RNDr. Petr Kosina, Ph.D. (assistant)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Wed 14:00–15:50 B411, Wed 16:00–16:50 B001
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 24 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Literature
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2010
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- Mgr. Jan Knotek (assistant)
RNDr. Petr Kosina, Ph.D. (assistant)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Tue 12:00–13:50 B411
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 23 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Literature
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2009
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Petr Kosina, Ph.D. (assistant)
RNDr. Antonín Pavelka, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Tue 8:00–10:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 22 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Semantic web
- Literature
- Teaching methods
- a lecture combined with practical exercises and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2008
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Tue 12:00–13:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 18 fields of study the course is directly associated with, display
- Course objectives
- Survey into machine learning in natural language processing. A project is as a part of this courses.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Semantic web
- Literature
- Assessment methods
- A project is as a part of the course.
- Language of instruction
- Czech
- Further Comments
- Study Materials
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2007
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Wed 9:00–11:50 B411
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 18 fields of study the course is directly associated with, display
- Course objectives
- Survey into machine learning in natural language processing. A project is as a part of this courses.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Semantic web
- Literature
- Assessment methods (in Czech)
- Součástí předmětu je projekt.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2006
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Wed 12:00–13:50 B411, Wed 14:00–14:50 B001
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 6 fields of study the course is directly associated with, display
- Course objectives
- Survey into machine learning in natural language processing. A project is as a part of this courses.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Semantic web
- Literature
- Assessment methods (in Czech)
- Součástí předmětu je projekt.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2005
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor) - Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics - Timetable
- Wed 14:00–16:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 6 fields of study the course is directly associated with, display
- Course objectives
- Survey into machine learning in natural language processing. A project is as a part of this courses.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text ming
- Web mining
- Semantic web
- Literature
- Assessment methods (in Czech)
- Součástí předmětu je projekt.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2004
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor) - Guaranteed by
- prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Thu 12:00–13:50 B410, Thu 16:00–17:50 B001
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 6 fields of study the course is directly associated with, display
- Course objectives
- Survey into machine learning in natural language processing. A project is as a part of this courses.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text ming
- Web mining
- Semantic web
- Literature
- Assessment methods (in Czech)
- Součástí předmětu je projekt.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2003
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- Mgr. Miloslav Nepil, Ph.D. (lecturer)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer) - Guaranteed by
- prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Tue 18:00–19:50 B001, Wed 10:00–11:50 X Datový projektor, Wed 10:00–11:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives (in Czech)
- Bude podán přehled učicích metod a systémů pro zpracování přirozeneého jazyka. Důraz je kladen na aplikace těchto metod. Součástí předmětu je projekt.
- Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing, syntactic analysis and learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text ming
- Web mining
- Semantic web
- Assessment methods (in Czech)
- Součástí předmětu je projekt.
- Language of instruction
- Czech
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
PA164 Machine learning and natural language processing
Faculty of InformaticsAutumn 2019
The course is not taught in Autumn 2019
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- Teacher(s)
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
- Guaranteed by
- doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 54 fields of study the course is directly associated with, display
- Course objectives
- Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.
- Learning outcomes
- A student will be able
- to pre-process text data for text mining;
- to build a system for analysis of text by means of machine learning;
- to understand research papers from this area;
- to write a technical report. - Syllabus
- Natural language processing(NLP). Corpora. Tools for NLP.
- Inroduction to machine learning
- Disambiguation. Morphological disambiguaiton and word-sense disambiguation
- Shallow parsing and machine learning
- Entity recognition and collocations
- Document categorization
- Information extraction from text
- Text mining
- Web mining
- Applications: text with spatio-temporal information, biomedical and biological texts.
- Literature
- recommended literature
- LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info
- MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
- not specified
- Mining text data. Edited by Charu C. Aggarwal - ChengXiang Zhai. New York: Springer Science+Business Media, 2012, xi, 522. ISBN 9781461432227. info
- Teaching methods
- a lecture combined with demonstrations and a work on a project
- Assessment methods
- Combination of written and oral examination. A defence of a project is as a part of the examination.
- Language of instruction
- Czech
- Further Comments
- The course is taught: every week.
- Teacher's information
- http://www.fi.muni.cz/~popel/lectures/ll/
- Enrolment Statistics (recent)