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Natural language processing

General data

Course ID:	1000-318bNLP
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Natural language processing
Name in Polish:	Natural language processing
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science Obligatory courses for 1st year Machine Learning
ECTS credit allocation (and other scores):	5.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Requirements:	Deep neural networks 1000-2M16GSN
Short description:	The goal of the course is to familiarize students with the theory, methodology, resources and tools for natural language processing. Lectures concern issues of computational linguistics (morphological, syntactic and semantic analysis), information extraction, text classification and automatic summarization of documents. The course will discuss the tools and language-specific issues for English and Polish. Knowledge of the theory of formal grammars and knowledge of machine learning techniques will be helpful.
Full description:	1. Introduction and Word Vectors 2. Subword Models 3. Linguistic Structure: Dependency Parsing 4. Recurrent Neural Networks and Language Models 5. Machine Translation, Seq2Seq and Attention 6. Attention Mechanism 7. Contextual Representations and Pretraining 8. Dialogue Systems 9. Natural Language Generation 10. Question Answering 11. Multitask Learning
Bibliography:	Dan Jurafsky and James H. Martin. Speech and Language Processing Jacob Eisenstein. Natural Language Processing Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning Delip Rao and Brian McMahan. Natural Language Processing with PyTorch
Learning outcomes:	Knowledge: the student * knows methodologies, topics, techniques and tools in natural language processing [K_W13]. Abilities: the student is able to * apply in practice techniques of natural language processing [K_U16]. Social competences: the student is ready to * critically evaluate acquired knowledge and information [K_K01]; * recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-* devised solution [K_K02]; * think and act in an entrepreneurial way [K_K03].
Assessment methods and assessment criteria:	Final grade is based upon the credit programming projects (computer programs) and written as well as oral exam.

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO LAB LAB WYK LAB LAB LAB TU W TH FR
Type of class:	Lab, 30 hours more information Lecture, 30 hours more information
Coordinators:	Paweł Budzianowski, Juliusz Straszyński
Group instructors:	Paweł Budzianowski, Mateusz Doliński, Jan Ludziejewski, Spyridon Mouselinos, Grzegorz Preibisch, Juliusz Straszyński, Emilia Wiśnios
Students list:	(inaccessible to you)
Examination:	Examination

Classes in period "Summer semester 2024/25" (future)

Time span:	2025-02-17 - 2025-06-08	Selected timetable range: weekly course term Navigate to timetable
Type of class:	Lab, 30 hours more information Lecture, 30 hours more information
Coordinators:	Juliusz Straszyński
Group instructors:	Gracjan Góral, Konrad Staniszewski, Juliusz Straszyński, Marcin Wierzbiński
Students list:	(inaccessible to you)
Examination:	Examination

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