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NCJSC «L.N. GUMILYOV EURASIAN NATIONAL UNIVERSITY» Module Handbook Educational program 7М06112 Artificial Intelligence Technologies Nur-Sultan 2022

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NCJSC «L.N. GUMILYOV EURASIAN NATIONAL UNIVERSITY»

Module Handbook Educational program

7М06112 Artificial Intelligence Technologies

Nur-Sultan

2022

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2

Contents Page

Module 1: EDUC 52003 Higher School Pedagogy 3

Module 2: PSYC 52004 Management psychology 3

Module 3: COMS 52005 Computational models 5

Module 4: EDUC 51101, EDUC 51201 , EDUC 51301 , EDUC Scientific-research work of graduate students

7

Module 5: COMS 53001 Formal Grammars 7

Module 6: COMS 53002 Ontologies, Semantic Technologies 8

Module 7: COMS 53003 Analysis and processing of large amounts of information 9

Module 8: COMS 53004 Machine learning and applications 11

Module 9: ENGL 52002 Foreign language (professional) 12

Module 10: PHIL 52001 History and Philosophy of Science 14

Module 11: COMS 52005 Intelligent information systems and technologies for their development

15 Module 12: COMS 53005 Statistical methods in Natural Language Processing 16 Module 13: COMS 53006 Machine learning algorithms for data processing 18

Module 14: COMS 53007 Speech Processing 19

Module 15: COMS 53008 Programming languages for data analysis and data processing 20

Module 16: COMS 62006 Decision support systems 21

Module 17: COMS 62007 Fuzzy modeling techniques 23

Module 18: TEIN 61001 Teaching internship 24

Module 19: COMS 63009 Methods of processing text corpora 27

Module 20: COMS 63010 Natural language processing software development methods 28 Module 21: COMS 63011 Artificial intelligence in project management 29 Module 22: COMS 63012 Development of algorithms for the implementation of machine

learning methods

30

Module 23: COMS 63013 Soft computing 31

Module 24: COMS 63014 Design and creation of artificial intelligence systems 32

Module 25: RhIN 61001 Research internship 34

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3 Module 1

Module code and name EDUC 52003 Higher Education Pedagogy Semester(s), when the module is taught 1

Responsible for module person Kalkeeva K.R.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (university component)

Teaching methods Traditional. Active and interactive teaching methods Workload (incl. contact hours, self-study

hours)

Total workload: 120 hours.

Lectures: 15 hours, practical: 22 hours, independent work of students: 83 hours.

Credit points (total by discipline) 4 ECTS Required and recommended

prerequisites for joining the module

Methods of studying private methods, Teaching technologies at the university.

Module objectives/intended learning outcomes

The development of professional and pedagogical thinking of teachers, the formation of scientific and pedagogical knowledge and skills necessary both for teaching activities and for improving general professional competence and pedagogical culture.

Content The proposed course is aimed at familiarizing undergraduates with scientific and pedagogical approaches to the organization of the pedagogical process, as well as with the principles of pedagogical activity carried out in the system of vocational education.

The sphere of professional pedagogical activity of the teacher is:

- higher educational institutions;

- colleges and other educational institutions;

- organizations and enterprises whose activities are related to various aspects of teaching. The presented discipline involves the creation of pedagogical conditions that ensure the development of the pedagogical position of masters, the formation of which determines the manifestation of the subjective characteristics of the teacher's personality in the system of vocational education.

Examination forms Matrix test

Study and examination requirements Visit to the MOOC platform. Studying the materials proposed on the basis of MOOC and PLATONUS, timely completion of tasks and, according to the test schedule, pass tests for the main course and individual work of students.

Technical, multimedia tools and software

Recording video lectures accompanied by slides and films. Study and feedback is carried out on the basis of MOOC and PLATONUS.

Reading list 1.Ahmetova G.K., Isaeva Z.A. Pedagogika: Uchebnik dlya magistratury universitetov. – Almaty: Қazaқ universitetі, 2018 – 328 s.

2.Pedagogicheskie tekhnologii: uchebnoe posobie dlya studentov pedagogicheskih special'nostej / pod red. V. S. Kukushina. — Rostov n/D: Mart, 2017. — 320 s.

3.Pedagogika vysshej shkoly: Uchebnik / Okolelov O.P. – M.:NIC INFRA-M, 2017. – 176 s.

4.Pedagogika vysshej shkoly: Uchebnik / K.R.Kalkeeva i dr – Astana- TOO «Master PO», 2017. – 253 s.

Module 2

Module code and name PSYC 52004 Management Psychology Semester(s), when the module is taught 1

Responsible for module person Mambetalina A.S.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (university component)

Teaching methods Group work. Problematic discussion. Search method. Design. Essay.

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4 Situational modeling. Text analysis. Creative writing.

Workload (incl. contact hours, self-study hours)

Total workload: 120 hours.

Lectures: 15 hours, practical: 22 hours, independent work of students: 83 hours.

Credit points (total by discipline) 4 ECTS Required and recommended

prerequisites for joining the module

Psychology, Rukhani zhangyru Module objectives/intended learning

outcomes

Objectives: to train Master’s degree students in management fundamentals that ensure the preservation of a certain structure, organized systems; maintaining the mode of management activities, the implementation of the program and management goals in professional activities.

Intended learning outcomes:

Know: the essence of the subject psychology of management; basic theories and concepts of management psychology in modern domestic and foreign science; methodological and technological features of management in the professional sphere.

Skills: be able to: analyze the processes of management activities; identify psychological control schemes; develop management schemes taking into account psychological patterns; determine the features of psychological interaction in management

Own: modern methods of socio-psychological analysis and diagnosis of the content and forms of management activities; methods of implementation of the main management approaches in the field of public procurement.

Content Introduction to the psychology of management. Leader personality.

Management styles, delegation and business career of the leader.

Psychology of staff motivation. Socialization of personality as a social phenomenon.

Characteristics of the process of adaptation of the subordinate to the conditions of the organization. The system of regulation of behavior and activity of the individual in the organization. Communication as a social phenomenon. Features of managerial communication. Communication between the leader and subordinates as the exchange of information, interaction and influence. Problems of interpersonal perception in managerial communication. Features of communication of the leader in a modern organization. Social organization as an object of management.

Psychology of conflict management in the activities of the leader. Social intelligence in the activities of the leader. Leader health. Prevention and overcoming stresses and life crises.

Examination forms Matrix test

Study and examination requirements It is necessary to participate in all types of control: current, intermediate, final, control of students' independent work.

The discipline determines the final grade, which consists of the results of the rating control and the exam, while 60% are rating control, 40% are the result of the exam. The exam must score at least 50% to successfully complete the course.

Technical, multimedia tools and software

Recording video lectures accompanied by slides and films. Study and feedback is carried out on the basis of MOOC and PLATONUS.

Reading list 1. Bazarov, T.YU. Psihologiya upravleniya personalom: Uchebnik i praktikum dlya akademicheskogo bakalavriata / T.YU. Bazarov. - Lyubercy: YUrajt, 2016. - 381 c.

2. Kozlov, V.V. Psihologiya upravleniya: Uchebnik / V.V. Kozlov. - M.:

Akademiya, 2016. - 240 c.

3. Mal'ceva YU. A, YAcenko O. YU. Psihologiya upravleniya.

Ekaterinburg : Izd-vo Ural. un-ta, 2016.— 92 s.

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5 4. Litvak, M.E. Komandovat' ili podchinyat'sya? Psihologiya upravleniya / M.E. Litvak. - Rn/D: Feniks, 2018. - 384 c.

5. Konovalenko, V. A. Psihologiya upravleniya personalom: uchebnik dlya akademicheskogo bakalavriata / V. A. Konovalenko, M. YU.

Konovalenko, A. A. Solomatin. — M.: Izdatel'stvo YUrajt, 2015. — 477 s. — (Seriya : Bakalavr. Akademicheskij kurs).

6. Bazarov T.YU. Psihologiya upravleniya personalom: uchebnik i praktikum dlya akademicheskogo bakalavriata.2015, Izdatel'stvo YUrajt M. - 381 s.

7. Kozlov, V.V. Psihologiya upravleniya / V.V. Kozlov. - M.: Academia, 2017. - 48 c.

8. Konovalenko, V.A. Psihologiya upravleniya personalom: Uchebnik dlya akademicheskogo bakalavriata / V.A. Konovalenko, M.YU.

Konovalenko, A.A. Solomatin. - Lyubercy: YUrajt, 2016. - 477 c.

9. Korolev, L.M. Psihologiya upravleniya: Uchebnoe posobie / L.M.

Korolev. - M.: Dashkov i K, 2016. - 188 c.

Module 3

Module code and name COMS 52005 Computational models Semester(s), when the module is taught 1

Responsible for module person Razakhova B.Sh.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (university component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Algorithms and data structure Module objectives/intended learning

outcomes

The main goal of the discipline is to study computational models of algorithms for teaching masters the problems of formalization of algorithms, directions for the development of the theory of algorithms and computational models, methods for constructing algorithms; to acquaint them with practical applications in the construction of various programming systems.

The goals of mastering the discipline:

- study of the basic principles of construction of calculation models and analysis of the results obtained;

- use of the acquired knowledge and practical skills in the study of disciplines of the basic and optional parts, as well as in writing master's theses;

- formation of algorithmic thinking;

- familiarization with the basic concepts of the theory of algorithms, computational models, methods of analysis and construction of various computational models;

- training and development of skills in the application of methods for constructing computational models for scientific research.

Know: analytical and numerical methods for solving various mathematical problems, basic techniques for processing experimental data, including using mathematical packages and systems;

Skills: Approximately solve real mathematical problems using the methods of computational mathematics and analyze the results obtained in the course of calculations; use the means of modern mathematical packages when making calculations;

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6 Competences: experience in analytical and numerical solution of various mathematical problems, skills in using the main methods of processing experimental data, including using mathematical packages and systems.

Content The discipline will allow you to study computational models and their features (Turing, Post machines, recursive functions, formal Markov orhythms, etc.) in practice.

1. Introduction to the theory of algorithms. The concept of formalization of algorithms. Classification of algorithms. Basic computational models.

2. Turing machine. The Church-Turing thesis. Variants and schemes of the Turing machine.

3. A way to prove the correctness of the program.

4. Recursive functions. Lambda calculation.

5. Verbal algorithms. Normal Markov algorithms.

6. Post's algorithm.

7. Procedural computational models.

8. Functional computing models.

9. Graph algorithms. Graphical analysis. Scheduled search. Efficient graph construction algorithms. Algorithms of Dijkstra, Prim and Kruskal.

10. Logical computational models, production computational models.

11. Neural network computing models.

12. Parallel algorithms. Polynomial and exponential algorithms.

13. Greedy algorithms.

14. Algorithms for prime and random numbers.

Genetic algorithms. Ant algorithms.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Semenova T.I., Kravchenko O.M., Shakin V.N. Computational models and algorithms for solving problems by numerical methods. Textbook:

MTUCI. – M.: 2017. – 84 p.

2. Stephen Skin. Algorithms. Development guide. 2nd edition, 720 pages, BHV-Petersburg, 2011

3. Krupsky V.N. Theory of algorithms: a textbook for students.

universities. - M.: Publishing house. Center "Academy", 2009. -208 p.

4. Aho A., Hopcroft J., Ulman J. Data structures and algorithms. - M .:

Williams Publishing House, 2012. - 384 p.

5. .Michael Sipser (2013). Introduction to the Theory of Computing (3rd ed.). Cengage Learning. 480 pp., published 2013 by Hsm Management.

ISBN 978-1-133-18779-0

6. Mar, Austin. "Quantum Computing in Complexity".

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7 Theory and Theory of Computing" (PDF). page 2. Retrieved June 7, 2014

Module 4

Module code and name EDUC 54101, EDUC 54201, EDUC 54301, EDUC 54101 Scientific-research work of graduate students

Semester(s), when the module is taught 1,2,3,4

Responsible for module person Research supervisors of undergraduates

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (university component)

Teaching methods Search, researcher

Workload (incl. contact hours, self-study hours)

Total workload: 720 hours. 1st semester: 210 hours, 2nd semester: 210 hours, 3rd semester: 120 hours, 4th semester: 180 hours.

Credit points (total by discipline) 24 ECTS Required and recommended

prerequisites for joining the module Module objectives/intended learning outcomes

The main purpose of the research work of the undergraduate is the development of the ability of independent research work, associated with the decision of complex professional tasks.

Learning Outcomes:

- to master the methods of search, processing and analysis of scientific literature on the topic of research;

- be able to formulate a statement of the task;

- to master the technology of conducting independent scientific research on the topic of the master's dissertation;

- to be able to justify the chosen scientific direction, to adequately select the means and methods for solving the tasks assigned to the scientific research; to conduct research in accordance with the developed scientific research program;

- be able to present the results obtained in the form of research reports and scientific publications

Content Analysis of problems and selection of research directions. Management of bibliographic work with a focus on modern information and communication technologies. Study of new scientific results in accordance with the theme of the master's dissertation. Compilation of scientific reviews on the topic of research. The solution of the assigned task is in accordance with the individual plan. Review and evaluation of research results: preparation of research results in the form of reports, articles, participation in scientific conferences and scientific seminars of the department

Examination forms report

Study and examination requirements Timely fulfillment of the individual plan of the master Technical, multimedia tools and

software

Search databases of scientific literature

Reading list On the topic of scientific research

Module 5

Module code and name COMS 53001 Formal Grammars

Semester(s), when the module is taught 1

Responsible for module person Sharipbaу A.A.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105

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8 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Algorithms and data structures, Theory of languages and automata, Discrete mathematics

Module objectives/intended learning outcomes

Undergraduates know how to provide an introduction to the key ideas and issues of computational linguistics from the position of formal grammar, to introduce the fundamental concepts of formal language, formal grammar and automata theory. Undergraduates know how to become familiar with standard terminology and the most important theoretical tools and concepts related to formal grammar. Undergraduates can classify machines by their ability to recognize languages, use automata to solve problems in computational linguistics.

Content Mathematical foundations of formal grammar and automation. Elements of mathematical logic. Elements of the theory of languages. Mechanisms of language generation Formal probabilistic grammar. Language recognition mechanisms. Automata and their classification.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Pentus AE, Pentus MR Theory of formal languages: textbook. - M.:

Publishing House of the Central Polytechnic Institute at the Faculty of Mechanics and Mathematics of Moscow State University, 2004. - 80 p.

2. Willem J.M. Levet. Introduction to the theory of formal languages and automata. Max Planck Institute for Psycholinguistics, Nijmegen.

3. John Benjamins Publishing. Amsterdam / Philadelphia, 2008, -404 p.

Module 6

Module code and name COMS 53002 Ontologies, Semantic Technologies Semester(s), when the module is taught 1

Responsible for module person Niyazova R.S.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105

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9 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Algorithms and data structures, Theory of languages and automata, Information and communication technologies, Intelligent information systems and technologies for their development

Module objectives/intended learning outcomes

Ontologies, semantic technologies: Undergraduates know the theory of ontological engineering. Undergraduates can study the methodological and technological foundations of designing semantic technologies.

Undergraduates have the opportunity to create an ontological model of the chosen subject area

Content Information, data and knowledge. The main ways of representing knowledge: frames, scenarios, products. semantic networks. Frame model. Ontology. RDF data representation format. SPARQL Query Language. OWL language constructs. Syntax. Descriptive logic EL and others. Knowledge base. Axioms and TBox. Statements and ABox.

Logical analysis.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. T. A. Gavrilova, D. V. Kudryavtsev, and D. I. Muromtsev, Knowledge Engineering. Models and methods: textbook. SPb., 2016. S. 300-301.

2. Semantic network for a working ontologist / ed. D. Allemang, J.

Hendler. Elsevier, 2011. 330 p.

3. Statistical Methods in Language and Linguistic Research Publisher:

Equinox Publishing Limited, October 2020 - 256 p.

Module 7

Module code and name COMS 53003 Analysis and processing of large amounts of information Semester(s), when the module is taught 1

Responsible for module person

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105

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10 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module Module objectives/intended learning outcomes

The purpose of mastering the discipline "Analysis and processing of large amounts of information" is the formation of competencies in the subject area related to solving the problems of collecting and analyzing huge amounts of structured or semi-structured information, developing data models based on it and extracting new knowledge.

Expected learning outcomes:

Know methods for solving problems of processing and analyzing big data Be able to develop and analyze conceptual and theoretical models of applied problems of big data analysis;

Be able to formulate machine learning problems for big data and offer solutions to the tasks.

Get hands-on big data skills with Python (R) programming language- based environments

Content Big data (Big Data): modern approaches to processing and storage. The problem of multiple data comparison. Analysis process. General scheme of analysis. Data extraction and visualization. Stages of modeling. Model building process. Forms of data representation, types and types of data.

Representations of datasets. Fundamentals of Hadoop, Sparkand other systems. Big Data Algorithms: Clustering, dimensionality reduction, popular subject sets and association rules. Analysis and processing of data from social networks. Application of big data processing algorithms in decision making problems. Architecture of big data processing systems.

Big data storage and processing technologies. Data retraining. Data visualization. Data understanding. The problem of retraining.

Regularization. Neural networks. Support vector machine.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Matloff Norman. The Art of R Programming. Dive into Big Data. - St.

Petersburg: Piter, 2019. 416 p. - ISBN 978-5-4461-1101-5. - URL:

2. Mirkin, B. G. Introduction to data analysis: textbook and workshop / B.

G. Mirkin. - Moscow: Yurayt Publishing House, 2020. 174 p. - ISBN

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11 978-5-9916-5009-0

3. Leskovec J. Mining of Massive Datasets [Electronic Resource] / Jure Leskovec, Anand Rajaraman, Jeffrey David Ulman. – 2nd. ed. - Cambridge: Cambridge University Press, 2014. - 482 p. - Authorized access: http://library.books24x7.com/toc.aspx?bookid=74213 (Online Digital Library "Books24x7").

4. Guller M. Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing [Electronic Resource] / Mohammed Guller. - Apress, 2015. - 290 p. - Authorized access: http://library.books24x7.com/toc.aspx?bookid=112020 (Online Digital Library "Books24x7").

5. Coelho Luis Pedro. Building machine learning systems in Python. - Moscow: DMK Press, 2016. - 302 p. - ISBN 978-5-97060-330-7

Module 8

Module code and name COMS 53004 Machine learning and applications Semester(s), when the module is taught 1

Responsible for module person Turebaуeva R.D.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Neural networks Module objectives/intended learning

outcomes

The purpose of studying the discipline is to study modern mathematical methods of machine learning designed to analyze data and build predictive models.

Discipline tasks:

Know the mathematical foundations of machine learning methods and related algorithms;

Be able to analyze, highlight features and combine machine learning methods;

Apply modern software environments and libraries that allow you to analyze, visualize data,

Possess the skills of practical use of machine learning methods in applied problems.

Content Problems of learning by precedents. Probabilistic formulation of the learning problem. Retraining, generalizing ability. Tasks of classification, regression recovery, ranking, clustering, association search. Formal model of machine learning. Basic algorithms for solving problems of classification and regression recovery. Visualization and clustering.

Artificial neural networks. Application of intelligent diagnostic methods in various subject areas.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem-

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12 solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Plas J. Vander Python for Complex Problems: Data Science and Machine Learning. - St. Petersburg: Peter, 2018. - 576 p.

2. Silen Davy, Meisman Arno, Ali Mohamed. Fundamentals of Data Science and Big Data. Python and data science. - St. Petersburg: Piter, 2017. -336 p.

3. A resource dedicated to machine learning, pattern recognition and data mining. - http://machinelearning.ru

Module 9

Module code and name ENGL 52002 Foreign language (professional) Semester(s), when the module is taught 2

Responsible for module person Sagimbayeva D.E.

Language of study English

Relationship with curriculum (cycle, component)

Basic (university component)

Teaching methods Group work. Problematic discussion. search method. Design. Essay.

situational modeling. Text analysis. Creative writing.

Workload (incl. contact hours, self-study hours)

Total workload: 120 hours.

Practical: 37 hours, independent work of students: 83 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Foreign language B2 Module objectives/intended learning

outcomes

The purpose of the discipline: The acquisition and improvement of competencies in accordance with international standards of foreign language education, allowing the use of a foreign language (the level of superbasic standardization (C1) as a means of communication for the successful professional and scientific activities of a future master who is able to compete in the labor market.

Intended learning outcomes:

- know the functional and stylistic characteristics of the scientific presentation of the material in the studied foreign language;

- be able to use general scientific terminology and the terminological sublanguage of the relevant specialty in a foreign language;

- freely read, translate original literature in the chosen specialty with subsequent analysis and evaluation of the extracted information;

- make a presentation of scientific research (at seminars, conferences, symposiums, forums);

- perceive by ear and understand public speeches in direct and indirect communication (lectures, reports, TV and Internet programs);

- have the skills to prepare written forms of presentation of information material in the specialty (scientific report, message, theses, abstract,

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13 abstract);

- have the skills to work with lexicographic sources in a foreign language (traditional and online).

Content Introduction to the course. Developing a focus. How to write master’s dissertation (introductory course). Sourcing information for your project.

Developing your project. Using evidence to support your ideas. Avoiding plagiarism. Paraphrasing and summarizing. Academic Style – some guidelines (Part I). Academic styles (Part II). Writing introductions.

Incorporating data and illustrations. Writing conclusions. Presentation skills. Preparing for conference presentation. Preparing for a conference presentation.

Examination forms Oral exam

Study and examination requirements Master’s degree students are required to attend practical classes in a foreign language and take an active part in the implementation of tasks for the individual works of Master’s degree students, the results of which are accepted by the teacher online or in the classroom of the university, depending on the type and form of the task.

Technical, multimedia tools and software

Databases: https://library.enu.kz/MegaPro/Web https://englishforacademicstudy.com

https://garneteducation.com http://presentationexpressions.com http://wiki.ubc.ca/Presentation_Skills https://global.oup.com/?cc=kz,

https://www.macmillanyounglearners.com/macmillanenglish/

https://www.britishcouncil.kz/kk https://edpuzzle.com/

Reading list 1. Sagimbayeva J.E., Moldakhmetova G.Z., Kurmanayeva D.K.

Tazhitova G.Z., Kassymbekova N.S. English course book for Master programme students of “Governmental audit and Financial control”

specialty

(from extended reading to academic writing) - Astana: L.N. Gumiloyv Eurasian National University, 2018. – 357p.

2. Sagimbayeva J.E., Kurmanayeva D.K., Tazhitova G.Z., Kassymbekova N.S. Electronic manual - English course book “Environment and Natural Resources Protection” for Master students of “Management and Engineering in the field of Environmental Protection educational programs” – Nur-Sultan, 2022

3. English for Academic Study. Joan McCormack and John Slaght - Extended Writing and Research Skills, University of Reading, 2012 – 152 p.

4. Tamzen Armer - Cambridge English for Scientists – Cambridge University Press, 2013 – 128 p.

4. Martin Hewings – Cambridge Academic English – Upper Intermediate- Cambridge University Press, 2012 – 176 p.

5. Dorothy E. Zemach, Lisa A. Rumisek - Academic Writing: from paragraph to essay. – London: Macmillan Education, 2016 - 130 p.

6. Academic Writing. A Handbook for International students. Stephen Bailey. Routledge. 2011

Module 10

Module code and name PHIL 52001 History and Philosophy of Science Semester(s), when the module is taught 2

Responsible for module person

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, Basic (university component)

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14 component)

Teaching methods Traditional. Active and interactive teaching methods Workload (incl. contact hours, self-study

hours)

Total workload: 120 hours.

Lectures: 15 hours, practical: 22 hours, independent work of students: 83 hours.

Credit points (total by discipline) 4 ECTS Required and recommended

prerequisites for joining the module

World History, Political Science, Sociology Module objectives/intended learning

outcomes

The main goal of the course is to develop undergraduates' interest in fundamental knowledge, stimulate the need for philosophical assessments of the formation and development of sciences, critical analysis of modern scientific achievements, develop a methodological culture of research work

Expected learning outcomes: Analyze the main worldview and methodological problems, including those of an interdisciplinary nature, studied in science at the present stage of its development and use the results professionally; understanding the dynamics of the development of science, its impact on the development of society, the formation of a holistic image of science, mastering the theory of method, mastering the logic and methodology of science; mastering in-depth skills in analyzing texts on philosophical problems of various sciences; critical reflection on various concepts of the growth of scientific knowledge; mastering the methodological culture of research work and the ability to use the acquired skills in their own professional activities.

Content Relationship between the philosophy of science and the history of science.

Philosophical ideas as heuristics of scientific research. The problem of demarcation in the philosophy of science. The genesis of science.

Discussions about the origin of science. The problem of scientific rationality. classical science. Scientific picture of the world. Ethos of classical science. Non-classical science and post-non-classical science.

Scientific picture of the world. The ethos of science. Philosophy of science: basic meanings. Problems of the boundaries of scientific knowledge in the philosophy of I. Kant. Positivist tradition Analytical philosophy and its influence on the philosophy of science. The transition from the logic of science to the history of science. The structure of scientific knowledge. Basic types of sciences. Types of cognitive procedures. Philosophy of natural sciences. Circle of problems of philosophy of natural sciences. Philosophy of technology and technical sciences. The role of technology in science. Information and computer technologies in non-classical technical sciences. Ecological aspects of the social assessment of technology. Specificity of socio-humanitarian knowledge. The problem of the formation of social theory. The theme of

"death of the subject" in postmodern philosophy. Time, space, chronotope. The problem of values. post-colonial studies.

Examination forms Oral exam

Study and examination requirements To successfully pass the final control, the undergraduate needs to know the terminology, theories and concepts of the discipline. Know personalities and their works. The code of conduct and ethics must comply with the requirements of the university. In this regard, marks are given from 0 to 100 points.

Technical, multimedia tools and software

Computer, projector. https://mooc.enu.kz/, https://moodle.enu.kz/

Reading list 1. Kanke V.A. Osnovnye filosofskie napravleniya i koncepcii nauki.

M.,2013

2. Kohanovskij V.A. Istoriya i filosofiya nauki.-M., - 2010

3. Klyagin N. Sovremennaya nauchnaya karta mira [Elektronnyj resurs]:

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15 uchebnoe posobie / N. Klyagin.- 1, 02 MB.- Moskva: Logos, 2017.- 186 s 4. Kun T. Struktura nauchnyh revolyucij. -M. AST.- 2015 ISBN 978-5- 17-089239-6 http://www.psylib.ukrweb.net/books/kunts01/index.htm 5. Filosofiya nauki: Obshchie problemy poznaniya. Metodologiya estestvennyh i gumanitarnyh nauk: hrestomatiya - M.: Progress-Tradiciya : MPSI : Flinta, 2005. - 992 s.

6. Nurmanbetova, D.N. Istoriya i filosofiya nauki [Tekst] / D.N.

Nurmanbetova.- Astana: ENU, 2012 Module 11

Module code and name COMS 52005 Intelligent information systems and technologies for their development

Semester(s), when the module is taught 2

Responsible for module person Kudubaeva S.A.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (university component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Computing models; Analysis and processing of large volumes of information.

Module objectives/intended learning outcomes

The discipline is aimed at studying the signs of intelligence of information systems; modern information technologies that provide intellectual properties; main classes of intelligent information systems: expert systems, systems with an intelligent interface, self-learning and adaptive systems, technology design features and tools and tools for the development of intelligent information systems. Undergraduates will acquire skills in the development of intelligent information systems.

Students have knowledge in the field of artificial intelligence systems and decision making, studied software for building intelligent systems for various subject areas. development of a conceptual model of ACS, focused on the management of educational and economic activities of the university;

Students are able to conduct experimental testing of the created system and develop recommendations for its use. Know the concepts and methods of creating IS based on the theory of artificial intelligence using a semantic-frame model of knowledge representation;

They have skills in the development of intelligent information management systems that effectively, efficiently and efficiently manage certain activities of a company, enterprise, and ensure the full satisfaction of information requests.

Content Signs of intellectuality of information systems; modern information technologies that provide the property of intellectuality; main classes of intelligent information systems: expert systems, systems with an intelligent interface, self-learning and adaptive systems; design features of intelligent information systems; technologies and development tools Classification of systems with artificial intelligence. The problem of knowledge representation in information systems. Production model of knowledge representation. Fundamentals of expert systems design. Fuzzy sets and fuzzy logic. Frames and semantic networks. ontological approach.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

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16 -20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Chan, K.C.C., 2004, Intelligent Information Systems: Course Notes, Department of Computer Science, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

2. Kukharenko B.G. Intelligent systems and technologies: textbook / B.G.

Kukharenko; - Moscow: Altair: MGAVT, 2015. - 115 p.

3. Batyrkanov, Zh.I. Development of an automated system for managing the educational process.//Izv. KSTU, Bishkek, 2019, No. 19. — P.115- 118.

4. Boskebeev, K.D. B85. Intelligent information systems // Monograph.

Information Center "Tekhnik", Bishkek, 2017. - P. 148.

5. Makarenko S. I. Intelligent information systems: textbook. - Stavropol:

SF MGGU im. M. A. Sholokhova, 2019.– 206 p.: ill

6. Gusarova N.F., Dobrenko N.V. Intelligent systems and technologies.

St. Petersburg. 2019. - 105 p.

7. Smagin A.A., Lipatova S.V., Melnichenko A.S. Intelligent information systems. Ulyanovsk 2017. - 197 p.

Module 12

Module code and name COMS 53005 Statistical methods in Natural Language Processing Semester(s), when the module is taught 2

Responsible for module person Kudubaeva S.A.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Algorithms and data structures, Theory of languages and automata, Information and communication technologies, Intelligent information systems and technologies for their development

Module objectives/intended learning outcomes

Undergraduates can study the methods of collecting, organizing and processing linguistic statistical data to identify existing patterns.

Undergraduates know the methodology of the linguistic mathematical-

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17 statistical method and its application

Content Probability theory and mathematical statistics in linguistics. Random value. Fundamentals of mathematical statistics. The subject of mathematical statistics. Statistical observation. Generalization and grouping of statistical materials in linguistics. Basic selection methods.

General and special methods used in linguistics. Practical use of statistical methods of linguistic research. Hypotheses and their application in linguistics. Types of statistical hypotheses. The level of static significance and quantitative assessment of the reliability of the established connection. Markov chains and processes in linguistics. Processes in linguistics in the language of Markov chains. Hidden Markov models in speech recognition. The problem of learning in a probabilistic setting.

Fundamentals of speech. Acoustics of speech production. Physical feasibility, sustainability. Introduction to "H - L" - processing. Signal smoothing. Using special tools. Review of algorithms for continuous speech reconstruction. Introduction to recognition using hidden Markov models, neural networks.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

Enterprise Architect. Microsoft project. Methodological developments, customized maps, interactive whiteboard, laptop, LCD projector

Reading list 1. Chan, K.C.C., Intelligent Information Systems: Course Notes, Department of Computer Science, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. 2004

2. Kukharenko B.G. Intelligent systems and technologies: textbook / B.G.

Kukharenko; - Moscow: Altair: MGAVT, 2015. - 115 p.

3. Batyrkanov, Zh.I. Development of an automated system for managing the educational process.//Izv. KSTU, Bishkek, 2019, No. 19. — P.115- 118.

4. Boskebeev, K.D. B85. Intelligent information systems // Monograph.

Information Center "Tekhnik", Bishkek, 2017. - P. 148.

5. Makarenko S. I. Intelligent information systems: textbook. - Stavropol:

SF MGGU im. M. A. Sholokhova, 2019.– 206 p.: ill

6. Gusarova N.F., Dobrenko N.V. Intelligent systems and technologies.

St. Petersburg. 2019. - 105 p.

7. Smagin A.A., Lipatova S.V., Melnichenko A.S. Intelligent information systems. Ulyanovsk 2017. - 197 p.

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18 Module 13

Module code and name COMS 53006 Machine learning algorithms for data processing Semester(s), when the module is taught 2

Responsible for module person Kintonova A.Zh.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Basic (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Computing models; Analysis and processing of large volumes of information.

Module objectives/intended learning outcomes

Study of machine learning algorithms for the implementation of machine learning methods; study and application of machine learning algorithms for data processing; application of developed technologies for machine learning and use of software for data analysis.

Content Introduction to machine learning algorithms. Data processing.

Modern algorithms and calculation methods. Linear regression.

Algorithms for the method of logistic regression. Logistic regression.

Algorithms for data processing. Logistic function. Algorithms for the method of K-close neighbors (KNN). Processing algorithms for the tree method of accepting solutions. Decision Trees and Random Forests Algorithms for Support Vector Machines

K-Means Clustering method. Principal Component Analysis method.

Vector Quantization Network Method (LVQ)

The Bagging method and the random forest. Software development tools for data analysis. The Busting and AdaBoost method. Software development tools for data analysis. Application of the method for linear discriminant analysis (LDA). Application of trees to the adoption of decisions.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

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19 Reading list 1. Aggarwal C.C. Data mining: the textbook. - Cham: Springer, 2016. -

734 p.

2. Kubat M. An introduction to machine learning / M. Kubat. - 2nd ed. - Cham: Springer, 2017. - 348 p.

3. Skobtsov Yu.A. Fundamentals of evolutionary calculations: textbook. - Donetsk: DonNTU, 2017. - 326p.

4. Kuhn M. Applied predictive modeling / М. - New York: Springer Science + Business Media, 2018. - 600 c.

5. Vyugin, VV Mathematical bases of machine learning and forecasting:

textbook / VV Vyugin. —Moscow: MCNMO, 2013. - 304 p Module 14

Module code and name COMS 53007 Speech Processing Semester(s), when the module is taught 2

Responsible for module person

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Algorithms and data structures, Theory of languages and automata, Information and communication technologies, Intelligent information systems and technologies for their development

Module objectives/intended learning outcomes

Students can express a speech signal in terms of its representations in the time and frequency domains and various ways of modeling it; derive expressions for simple functions used in speech classification applications; synthesize flowcharts for speech applications, explain the purpose of the various blocks and describe in detail the algorithms that could be used to implement them; implement components of speech processing systems.

Content The problem of machine learning in a probabilistic environment.

Fundamentals of speech. Acoustics of speech production. Physical feasibility, sustainability. Introduction to "H - L" - processing. Signal smoothing with special tools. Overview of algorithms for continuous speech recognition. Introduction to recognition using hidden Markov models, neural networks.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

-40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

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20 The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Actual problems of modern linguistics: textbook. - Moscow: Flint;

Nauka, 2017 - - 412 p.

2. Hobson Lane, Hannes Hapke, Cole Howard X68 Natural language processing in action. - St. Petersburg: Peter, 2020. - 576 p.: ill. - (Series

"For professionals"). ISBN 978-5-4461-1371-2

3. Reese R. Natural language processing in Java / per. from English by A.V. Snastina. -M.: DMK Press, 2016. - 264 p.

Module 15

Module code and name COMS 53008 Programming languages for data analysis and data processing

Semester(s), when the module is taught 2

Responsible for module person Turebaуeva R.D.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (elective component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module Module objectives/intended learning outcomes

The main goal of this discipline is the formation of undergraduates' ability to apply big data processing technologies and machine learning to solving applied problems.

Intended learning outcomes:

Be able to visualize data, process data, perform exploratory data analysis using the basic tools of R, Python, Scala, MATLAB, etc.

Be able to formulate algorithms, choose the right big data analysis tool, choose the right big data storage technology.

Analyze data received from external sources, justify conclusions, work with data sets and find patterns in numbers.

Have the skills to work with the main software technologies and methods of data mining, the use of modern software packages for data mining on a computer

Content Language and environments for statistical computing and graphics in data science. SAS is a programming language for extracting, modifying and manipulating data from various sources for advanced statistical analysis.

General purpose programming languages to perform the ETL (Extract- Transform-Load) process. Java, Julia - languages for writing production- specific ETL codes and computationally intensive machine learning algorithms. Scala is a language that allows you to use both object-oriented and functional approaches to process a large amount of disparate data.

MATLAB is a programming language and environment for iterative analysis and process design.

Examination forms Written exam

Study and examination requirements The final mark will be weighted as follows:

-20 degrees for assignments and Class work;

-40 degrees for two intermediate controls;

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21 -40 degrees for final Written Exam.

Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.

The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod

Technical, multimedia tools and software

e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector

Reading list 1. Philip R. Holland. SAS Programming and Data Visualization Techniques. 2015.- 245 р. ISBN: 1484205693

2. Ken Kleinman, Nicholas J. Horton. SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition. Chapman and Hall/CRC. 2014, 428 rubles ISBN:1466584491

3. D. Silen, A. Meisman, M. Ali - Fundamentals of Data Science and Big Data. Python and data science. SPb.: Peter. 2018, 336 p.

4. Odersky M., Spoon L., Wenners B. O-41 Scala. Professional programming. 4th ed. - St. Petersburg: Peter, 2021. - 720 p.: ill. - (Series

"Programmer's Library"). ISBN 978-5-4461-1827-4

5. Rutkovsky, L. Methods and technologies of artificial intelligence / L.

Rutkovsky. - M .: Hot line - Telecom, 2010. - 520 p.

Module 16

Module code and name COMS 62006 Decision support systems Semester(s), when the module is taught 3

Responsible for module person Niyazova R.S.

Language of study Kazakh/Russian/English

Relationship with curriculum (cycle, component)

Profile (university component)

Teaching methods Interactive, case study, student-centered learning, problematic discussion.

Workload (incl. contact hours, self-study hours)

Total workload: 150 hours.

Lectures: 15 hours, practical: 30 hours, independent work of students: 105 hours.

Credit points (total by discipline) 5 ECTS Required and recommended

prerequisites for joining the module

Intelligent information systems and technologies for their development Module objectives/intended learning

outcomes

Objectives: to teach undergraduates theoretical knowledge in the field of managerial decision-making, familiarization with methods for solving practical problems of decision-making, the formation of practical skills in using specialized software

Know: capabilities of decision support systems (DSS); basic theoretical provisions and concepts of the logic of decision-making processes in the economy; basics of modeling management decisions; methods of execution of decisions at various stages of the decision-making cycle;

types of information and instrumental support for a decision maker (DM), criteria for choosing DSS tools; multi-criteria decision-making methods.

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