Courses in Detail

The following overview summarizes main courses covering LT and CS modules (may be subject of marginal changes – please check in Student Information System).

The LT Courses in Detail

Statistical Methods in Natural Language Processing I

code: NPFL067
module: LT-M1, LT-M2
semester: winter credits: 6 ECTS, obligatory
type: C+Ex

Introduction to formal linguistics and the fundamentals of statistical natural language processing, including basics of Infromation Theory, Language MOdeling and Markov Models. Continues as Statistical Methods in Natural Language Processing II.

Statistical Methods in Natural Language Processing II

code: NPFL068
module: LT-M4
semester: summer credits: 6 ECTS
type: C+Ex

Continuation of Statistical Methods in Natural Language Processing I. Introduces the notion of linguistic experiment and its evaluation. The role of corpora in statistical NLP. Standard NLP tasks (tagging, parsing) are explained and methods presented. Short introduction to Statistical Machine Translation.

General Linguistics

code: NPFL106
module: LT-M1
semester: summer credits: 3 ECTS,
type: MC

The course will help students to get familiar with advanced topics in linguistics, especially syntax, semantics, historical linguistics and psycholinguistics. Most topics will be addressed both from the perspective of traditional linguistics and from the formal perspective of mathematics and computer science. Students are expected to have basic knowledge of linguistics, for example as provided by NPFL063 - Introduction to General Linguistics.

Language Data Resources

code: NPFL070
module: LT-M1
semester: summer credits: 5 ECTS
type: MC

The goal of the seminar is to provide students with the survey of the field of Language Resources. Selected types of linguistic annotations will be described, with emphasis on annotating textual data (morphological categories, constituency and dependency syntactic trees, anaphora, discourse structure, word-sense disambiguation, parallel-text alignment etc.) and lexical data (wordnets, translation dictionaries, valency lexicons etc.). Leading projects for English, Czech, and some other languages will be used for illustration.

Introduction to Formal Linguistics

code: NPFL006
module: LT-M1
semester: winter credits: 3 ECTS
type: Ex

A survey and critical discussion of the main trends of developments in theoretical and formal linguistics from N. Chomsky to the most recent approaches.

Fundamentals of Speech Recognition and Generation

code: NPFL038
module: LT-M1
semester: winter credits: 4 ECTS
type: MC

This course deals with speech recognition and generation tasks and feature extraction of voice and utterance characteristics. Of particular interest will be topics related to Hidden Markov Models as applied to speech (FFT, n- dimensional clustering, Gaussian mixtures, parameter value extraction from data, phonetic representation, prosodic analysis etc.). Preparation and training of own speech recognition models.

Morphological and Syntactic Analysis

code: NPFL094
module: LT-M2
semester: winter credits: 3 ECTS
type: MC

Basic methods and algorithms used for morphemic segmentation, morphological and syntactic (constituency-based, dependency-based, tectogrammatical) analysis of natural languages. We will try out some of the approaches on an unknown language, as student mini-projects during the semester. Credits will be awarded for contribution to these mini-projects.

Computational morphology

code: NPFL096
module: LT-M2
semester: summer credits: 4 ECTS
type: Ex

Introduction to the methods of processing morphology of natural languages. The course covers both supervised and unsupervised methods of morphological analysis, morpheme segmentation, lexicon creation, etc. Most of the course consists of discussion of important papers in the field. Students will replicate or extend a system from one of the papers.

NLP Applications

code: NPFL093
module: LT-M2
semester: summer credits: 5 ECTS
type: MC

The main goal of the course is to introduce basic types of natural language processing (NLP) applications and to give the students a chance to work with some of those applications in seminars. The course will concern machine translation, machine aided human translation tools, localization tools, information retrieval and extraction, question answering, speech recognition, spelling and grammar checking, generation etc.

Introduction to Computer Linguistics

code: NPFL012
module: LT-M2
semester: winter credits: 3 ECTS
type: Ex

The main goal of this course is to provide an overview of individual subfields of computational linguistics. Main issues being solved by these subfields are also mentioned. Among the subfields the course stresses are machine translation, syntactic parsing, morphology and corpus linguistics.

Linguistic Theory and Grammar Formalisms

code: NPFL083
modules: LT-M2
semester: summer credits: 6 ECTS
type: C+Ex

The aim of this course is to bridge the gap between theoretically motivated description of linguistic phenomena and a corresponding implementation in formal grammars. Following an overview of formal frameworks coupled with specific theories - Categorial Grammar (CG), Tree Adjoining Grammmar (TAG), Lexical Functional Grammar (LFG), Head-driven Phrase Structure Grammar (HPSG) - and formal aspects of other theoretical frameworks (Chomskyan and dependency-based tradition), the students will be introduced to the core principles of HPSG both as a theory and as a formalism, based on examples of relevant phenomena in English, Czech and other languages. In parallel with the classroom presentations and discussions the students will develop corresponding grammars of increasing complexity, using the system Trale as the grammar writing environment.

Information Structure of Sentence and Discourse Structure

code: NPFL082
module: LT-M3
semester: summer credits: 3 ECTS
type: Ex

The information structure of a sentence (or according to the Czech traditional terminology, the topic-focus articulation – a division of a sentence into the part the sentence is about and the focus part) is an important starting point for an analysis of higher units than a sentence, namely discourse (text) and its structure. The course will deal with a semantic consequences of the topic-focus articulation, with its treatment in the formal description of a language and with langauge-dependent means of the expression of the topic-focus articulation. Special attention will be paid to the way in which this aspect of the sentential structure is treated in the electronic Prague Dependency Treebank and to the question how the corpus can be used for a verification of linguistic hypotheses. In the second part of the course, the questions of the discourse structure will be discussed, especially how the observations of the sentential structure can be used in the study of different aspects of discourse. In this part, we will use the material from Prague Dependency Treebank as well, namely the annotation of coreferential and anaphoric relations.

Prague Dependency Treebank

code: NPFL075
modules: LT-M3, LT-M4
semester: summer credits: 6 ECTS
type: C+Ex

The subject should make the students familiar with Prague Dependency Treebank (PDT 2.0) project, starting from its theoretical base, including particular layers of annotation and ending with the way how important linguistic phenomena are represented. Emphasis is also placed on annotation schemata and data format, on familiarization with useful tools and practical work with the treebank.

Lexical Analysis of Natural Language

code: NPFL088
module: LT-M3
semester: summer credits: 3 ECTS
type: C

Introduction to computational aspects of lexical semantics. Basic concepts and issues. Fundamental approaches to lexical disambiguation.

Tools of Automated Translation

code: NPFL015
module: LT-M4
semester: winter credits: 3 ECTS
type: C

The course concerns the history of machine translation and its contemporary trends. The historical part will introduce the most famous translation systems (TAUM-METEO, Systran, Eurotra, ETAP), with a special attention devoted to the systems involving Czech (Ruslan, Česílko). The students will learn about individual methods used in MT, especially with the traditional rule-based approach, Example-based MT and Knowledge-based MT.

Statistical Machine Translation

code: NPFL087
module: LT-M4
semester: summer credits: 6 ECTS
type: C+Ex

Participants of the seminar will get closely acquainted with methods of machine translation (MT) that rely on automatic processing of (large) training data as well as with open-source implementations of these methods. We will cover a range of approaches starting with linguistically uninformed "phrase-based" MT up to surface and deep syntactic MT. The final grade will reflect mainly students' own contributions: either experimental results, implemented tools or modifications to existing systems, or survey reports.

Algorithms in Speech Recognition

code: NPFL079
module: LT-M4
semester: summer credits: 6 ECTS
type: C+Ex

The course presents recent methodologies and software toolkits for speech recognition. Students will learn how to develop systems of automatic speech recognition and transcription, computer dialogue systems and speaker identification. The course shows principles, preparation and decoding algorithms of statistical acoustic and language models (HMM, n-gram and structured language models, final state transducers, graphical models, Viterbi dynamic programming, heuristic hypothesis search strategies, stack decoder). This course can be preceded by PFL038 and combined with PFL067, PFL068.

The CS Courses in Detail

Data Structures I

code: NTIN066
module: CS-M1
semester: winter credits: 3 ECTS, obligatory
type: Ex

The lecture continues on the lectures Algorithms and Data Structures I and II and Programming I and II from the bachelor study. The lecture is devoted to two basic data structures, hashing and $(a,b)$-trees (the second structure is also called $B$-trees). We describe basic properties of these structures and we investigate their complexity. The end of the lecture compares basic sorting algorithms.

NLP Technology

code: NPFL092
module: CS-M1
semester: winter credits: 4 ECTS, obligatory
type: MC

The aim of the course is to get students familiar with basic software tools used in natural language processing.

Information Retrieval

code: NPFL103
module: CS-M1
semester: winter credits: 3 ECTS
type: C

The course will introduce modern methods and principles applied in the field of information retrieval in large data collections.

Introduction to Complexity and Computability Theory

code: NTIN090
module: CS-M2
semester: winter credits: 5 ECTS, obligatory
type: C+Ex

This is a basic course on the computability theory and computational complexity. Roughly the first half of the course is devoted to the introduction to computability theory: Turing machines. Computable functions. Recursive and recursively enumerable sets. Undecidable problems. Recursion theorem. The second half of the course is devoted to the study of space and time complexity classes: Equivalence of PSPACE and NPSPACE, Class NP. Polynomial reducibility among problems. Proofs of NP-completeness. Approximation algorithms and approximation schemes.

Practical Fundamentals of Probability and Statistics for Computational Linguistics

code: NPFL081
module: CS-M2
semester: winter credits: 3 ECTS
type: C

The aim of the course is to introduce elementary probabilistic and statistical principles, techniques and methods which are used in solving computational linguistics (natural language processing) tasks. An essential part of the course is active work with data and introduction to workflow in R while solving a given task. A part of the course will consist of individual study of mutually agreed selected materials.

Constraint Programming

code: NOPT042
module: CS-M3
semester: winter credits: 6 ECTS
type: C+Ex

The course gives a survey of constraint satisfaction techniques. The focus is on algorithms for constraint satisfaction, such as search algorithms (depth-first search and local search) and propagation algorithms (arc and path consistency). Solving over-constrained problems is also discussed as well as some modeling techniques are covered.

Planning and Scheduling

code: NAIL071
module: CS-M3
semester: summer credits: 3 ECTS
type: Ex

The course gives an introduction to planning and scheduling. It is focused on the algorithms for solving planning and scheduling problems with emphasis on using constraint-based techniques.

Introduction to Machine Learning (in Computational Linguistics)

code: NPFL054
module: CS-M2, CS-M4
semester: winter credits: 6 ECTS
type: C+Ex

This one-semester introductory course provides theoretical background of and key algorithms from the field of machine learning (ML) explained independently on a broad spectrum of multidisciplinary applications the ML takes place in. The seminars are application-dependent and they accompany the lecture sessions. The aim of the seminars is an acquisiton of practical experience from application of ML approaches on problems from natural language processing. This course is intended for students from master and doctoral study programmes. Introduction to probability and stastistics required. The lecture will be taught either in Czech or in English, based on students' preference.

Modern Methods in CL

code: NPFL095
module: CS-M4
semester: winter,summer credits: 3 ECTS
type: C

Presentation and discussion of important scientific papers from the area of contemporary computational linguistics, machine learning and related areas. During the semester, each participant is supposed to give talks about selected papers.

Statistical dialog systems

code: NPFL099
module: CS-M4
semester: summer credits: 5 ECTS
type: C+Ex

The course aims to provide students with basic understanding of methods and approaches to building spoken dialog systems. The course will emphasize and discuss the importance of statistical techniques in construction of natural and robust dialog systems.

Artificial Intelligence I

code: NAIL069
module: CS-M4
semester: winter credits: 3 ECTS
type: Ex

An introductory course on artificial intelligence with the focus on basic concepts and methods. Intelligent agents, environment, and structure of agents. Problem solving by search (DFS, BFS, ID, A*, IDA*, local and on-line search, heuristics). Constraint satisfaction. Games (minimax, alfa-beta pruning).

String Algorithms

code: NTIN087
module: CS-M3
semester: winter credits: 3 ECTS
type: Ex

A survey of algorithms and data structures for efficient computation of patterns in strings with applications.

The Additional Course in Detail

Software Project

code: NPRG027, NPRG023
module: additions
semester: winter/summer credits: 15 ECTS, obligatory
type: C

The goal of this course is to practice a team work in a group software project. The work on the project is finished by a public presentation. The projects topics may come from various fields, therefore the credits are divided among the corresponding modules with regard to a concrete project topic.

Czech for Foreigners

code: ---
module: additions
semester: winter/summer credits: at least 3 ECTS, obligatory
type: C / Ex