PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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:
PA164/01: Wed 25. 9. to Wed 18. 12. each odd Wednesday 18:00–19:50 A320, V. Nováček
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.
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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:
PA164/01: Wed 27. 9. to Wed 6. 12. each odd Wednesday 18:00–19:50 C525, V. Nováček
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.
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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:
PA164/01: each even Wednesday 18:00–19:50 B116, V. Nováček
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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:
PA164/01: Thu 23. 9. to Thu 2. 12. each even Thursday 18:00–19:50 B117, V. Nováček
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
Assessment methods (in Czech)
Součástí předmětu je projekt.
Language of instruction
Czech
Teacher's information
http://www.fi.muni.cz/~popel/lectures/ll/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
Assessment methods (in Czech)
Součástí předmětu je projekt.
Language of instruction
Czech
Teacher's information
http://www.fi.muni.cz/~popel/lectures/ll/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
Assessment methods (in Czech)
Součástí předmětu je projekt.
Language of instruction
Czech
Teacher's information
http://www.fi.muni.cz/~popel/lectures/ll/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
  • 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
Assessment methods (in Czech)
Součástí předmětu je projekt.
Language of instruction
Czech
Teacher's information
http://www.fi.muni.cz/~popel/lectures/ll/
The course is also listed under the following terms Autumn 2003, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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
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/
The course is also listed under the following terms Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

PA164 Machine learning and natural language processing

Faculty of Informatics
Autumn 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/
The course is also listed under the following terms Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (recent)