Научно-исследовательский семинар "Интеллектуальные системы и структурный анализ"

Магистратура 2021/2022

Статус: Курс обязательный (Науки о данных (Data Science))

Направление: 01.04.02. Прикладная математика и информатика

Кто читает: Департамент анализа данных и искусственного интеллекта

Где читается: Факультет компьютерных наук

Когда читается: 2-й курс, 1, 2 модуль

Формат изучения: без онлайн-курса

Преподаватели: Гончарова Елизавета Федоровна, Яковлев Константин Сергеевич

Прогр. обучения: Науки о данных

Язык: английский

Кредиты: 8

Контактные часы: 48

Full Syllabus

Abstract

The discipline goal is to develop students' professional skills required for independent analytical work in applied fields of the computer science. Also, this course aims to improve skills of students in developing their research projects related with dialogue systems and chat bots. This course focuses on analysis of scientific and industrial linguistic system developing and motivates visiting different scientific colloquium at the university, especially at the faculty of computer science.

Learning Objectives

The Research Seminar should help students to form the basic skills training to make and present their own research, motivate to engage in the scientific activity.
know some algorithms used in cryptography and based on the use of number- theoretic constructions
to understand the fundamental concepts of number theory and the ways in which they can be used in cryptography.
know the fundamental concepts of the Semantic Web, the structure of RDF and related technologies, such as RDFa and SPARQL
know the ontology language OWL 2 and its profiles, principles of ontology- based data access, the foundations of the basic representation of knowledge and formalisms of reasoning, such as the description logic
use terminology and methods for writing queries using SPARQL, writing ontologies in OWL 2

Expected Learning Outcomes

Formulate the task and goals for an independent research and/or scientific programing system development.
Know basic principles of argumentation for chat bot.
Know basic principles of assuring chat bot relevance at syntactic level.
Know basic principles of building taxonomy and thesaurus for chat bots.
Know basic principles of chat bot content processing pipeline.
Know basic principles of developing task-oriented linguistic dialogue systems.
Know basic principles of discourse-level dialogue management.
Know basic principles of discourse-level structures.
Know basic principles of managing rhetorical agreement in dialogue utterances.
Know basic principles of Q/A for Bots.
Know fundamental approaches to natural language understanding and dialogue management in the task-oriented dialogue systems.
Know main principles of social bots.
Know main principles of task-oriented bots.
Prepare a presentation based on his research and/or scientific programing system.
Students should be able to evaluate the greatest common divisor of two integers using Euclidean algorithm.Students should be able to solve linear equation ax+by=c using Euclidean algorithm.
Students should be able to evaluate the value of Legendre symbol.
Students should be able to find a continued fraction expansion of rational numbers and of quadratic irrationals. Students should be able to evaluate quadratic irrational by knowing its continued fraction expansion.
Students should be able to work with congruences and to apply theorem of Ferma and Euler
Students understand and use the ontology language OWL 2 and its profiles.
Students understand fundamental concepts, advantages and limitations of Semantic Technologies.
Students understand the basics of knowledge representation with description logics.
Students understand the principles of ontology-based data access and integration.

Course Contents

Euclidean algorithm.
A basic chat bot
Continued fractions
Social Bots
Prime Numbers
Task-oriented Bots
Multiplicative functions
NL Understanding
The ring of integers modulo N
Assuring chat bot relevance at syntactic level
The Quadratic reciprocity law
Q/A for Bots: Semantic headers and semantic skeletons
Primitive roots.
Learning Discourse-level structures
Introduction to mathematical cryptography
Building taxonomy and thesaurus for chat bots
Integer factorization
Chat bot content processing pipeline
Managing Rhetorical Agreement in Dialogue Utterances
The Semantic Web and Knowledge Graphs introduction.
Discourse-level Dialogue management
Ontologies in Description Logics. Lightweight description logic EL and Snomed CT. Syntax and semantics.
Data for chat bot training
DL-Lite and Schema.org.
Argumentation for chat bot
Expressive description logics ALC and its extensions.
The Web Ontology Language OWL.
First-Order Predicate Logic (FOPL) as an Ontology Language.
Query answering over data and knowledge bases.
Ontology Based Data Access.

Assessment Elements

контрольная работа
In-class test
In-class written tests. Preparation time – 80 min. 3rd module.
Exam
Written exam. Preparation time– 120 min. 4th module. Оценка за дисциплину выставляется в соответствии с формулой оценивания от всех пройденных элементов контроля. Экзамен не проводится.
Report
Discussion
Presentation
Presentation of the programming project, paper, dialogue system, or dialogue platform. Speaking time is up to 30 min.

Interim Assessment

2020/2021 4th module
0.3 * In-class test + 0.4 * контрольная работа + 0.3 * Exam
2021/2022 2nd module
Оfinal= 0.4•Оpresentation+0.5•Оdiscussion+0.1•Оreport

Bibliography

Recommended Core Bibliography

J.H. Silverman, Jill Pipher, Jeffrey Hoffstein. An Introduction to Mathematical Cryptography. Springer-Verlag New York 2008
Manning C. D., Schutze H. Foundations of statistical natural processing. – 1999. – 719 pp.
William Stein. Elementary Number Theory: Primes, Congruences, and Secrets. Springer, New York, NY

Recommended Additional Bibliography

Perkins J. Python text processing with NLTK 2.0 cookbook. – Packt Publishing Ltd, 2010. – 336 pp.
Staab S., Studer R. Handbook on ontologies. – Springer, 2009. – 811 pp.

Course Syllabus