FI:PA164 Learning and natural language - Course Information
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).
Taught in person. - 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
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Czech Language with Orientation on Computational Linguistics (programme FF, B-FI)
- Discrete algorithms and models (programme FI, N-TEI)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics (eng.) (programme FI, D-IN4)
- Informatics (programme FI, D-IN4)
- Informatics (programme FI, N-IN)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Technologies (eng.) (programme FI, D-IN4)
- Computer Systems and Technologies (programme FI, D-IN4)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- 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/
- Enrolment Statistics (Autumn 2022, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2022/PA164