Methodological support. Master’s Degree. Intelligent Data Analysis

Course materials

1. Chip Huyen. Designing Machine Learning Systems. O’Reilly Media, Inc. – 2022. – ISBN: 9781098107963.

2. Andrew P. McMahon, Adi Polak . Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples. Packt Publishing; 2nd edition – 2023. – ISBN 978-1837631964.

3. https://www.datascience-pm.com/crisp-dm-2/

4. MLOps: Continuous delivery and automation pipelines in machine learning.

5. https://cs329s.stanford.edu

6. https://dvc.org/doc

7. https://mlflow.org/

8. https://docs.wandb.ai/guide

9. Attacking Machine Learning with Adversarial Examples.

Recommended reading

10. How ML Breaks: A Decade of Outages for One Large ML Pipeline.

11. https://realpython.com/python-data-version-control/

12. http://mlwave.com/kaggle-ensembling-guide/

13. https://scikit-learn.org/ stable/modules/ensemble.html

14. https://www.youtube.com/watch?v=dWhdWxgt5SU

15. “Interpretable Machine Learning” book.

16. A Survey Of Methods For Explaining Black Box Models.

17. Techniques for Interpretable Machine Learning.

18. https:// www.kaggle.com/ code/ samuelcortinhas/ weights- biases -tutorial -beginner

Course materials

1. Кононова К. Ю. Машинне навчання. – Харків: ХНУ ім. В. Н. Каразіна, 2020. – 301 с.

2. G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning with Applications in R. – New York: Springer, 2015. – 440 с. – ISBN 978-146-147-138-7.

3. P. Harrington. Machine Learning in Action. – Shelter Ireland, NY: Manning, 2012. – 400 с. – ISBN 978-966-423-364-1.

4. О. В. Бісікало,Формальні методи образного аналізу та синтезу природно- мовних конструкцій. Монографія, Вінниця: ВНТУ, 2013, – 316 с. – ISBN 978-966-641-528-1.

5. Principles of Natural Language Processing by Susan McRoy is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted. – ISBN 978-1-7376595-0-1, EPub – ISBN 978-1-7376595-1-8.

Recommended reading

6. A. Burkov. Machine Learning Engineering. – True Positive Inc., 2020. – 310 p. – ISBN 978-199-957-957-9.

7. M. Kubat. An Introduction to Machine Learning. – Springer Cham, 2021. – 458 p. – ISBN 978-303-081-935-4.

8. S.T. Wierzchoń, M.A. Kłopotek. Modern Algorithms of Cluster Analysis – Springer, Cham, 2018. – 421 p. – ISBN 978-3-319-69308-8.

9.S.T. Wierzchoń, M.A. Kłopotek. Modern Algorithms of Cluster Analysis – Springer, Cham, 2018. – 421 p. – ISBN 978-3-319-69308-8.

10. M. Kubat. An Introduction to Machine Learning. – Springer Cham, 2021. – 458 p. – ISBN 978-303-081-935-4.

Internet resources

11. https:// en.wikipedia.org/wiki/Natural_language_processing

12. https:// www.deeplearning.ai /courses /natural- language-p rocessing -specialization/

Course materials

1. Fundamentals and Methods of Digital Signal Processing: From Theory to Practice: Textbook / Compiled by: Yu. O. Usenko, M. S. Gavryliak, M. V. Talakh, V. V. Dvorzhak. – Chernivtsi: Chernivtsi National University named after Yu. Fedkovych, 2021. – 308 p. – ISBN 978-966-423-639-0.

2. T. Holton. Digital Signal Processing: Principles and Applications. – Cambridge: Cambridge University Press, 2021. – 1058 p. – ISBN 978-110-841-844-7.

3. S. Palani. Principles of Digital Signal Processing. – Springer Cham, 2022. – 677 p. – ISBN 978-303-096-321-7.

Recommended reading

4. S. Esakkirajan, T. Veerakumar, B. Subudhi. Digital Signal Processing. – McGraw-Hill, 2021. – 579 p. – ISBN 978-935-460-029-6.

5. A.-H. Najmi, T. K. Moon. Advanced Signal Processing: A Concise Guide. – McGraw-Hill, 2020. – 352 p. – ISBN 978-126-045-893-0.

6. P.S.R. Diniz. Signal Processing and Machine Learning Theory. – Academic Press, 2023. – 1234 p. – ISBN 978-032-391-772-8.

Internet resources

7. https:// en.wikipedia.org/wiki/ Digital_ signal _processing

Course materials

1. Building realistic social graphs based on public social network pages.

2. Graph theory and social networks.

3. Mazurenko, V. & Shtovba, Serhiy. (2015). REVIEW OF SOCIAL NETWORK ANALYSIS MODELS. Bulletin of Vinnytsia Polytechnic Institute.

https:// www.researchgate.net /publication / 279535422_OGLAD_MODELEJ_ANALIZU_SOCIALNIH_MEREZ

https:// pzs.dstu.dp.ua/ DataMining/ social /bibl/ SNA.pdf

4. Kormann, T., Leizerson, C., Rivest, R., Stein, C. Introduction to Algorithms. Kyiv: K. I. S., 2019. 1288 p. – ISBN 978-617-684-239-2.

5. Network Science by Albert-Laszlo Barabasi

Recommended reading

6. Discrete Mathematics: Lecture Notes (Part 1) [Electronic resource]: textbook for students majoring in 113 “Applied Mathematics,” educational program “Data Science and Mathematical Modeling” / O.L. Temnikova; Igor Sikorsky Kyiv Polytechnic Institute. – Kyiv: Igor Sikorsky Kyiv Polytechnic Institute, 2021. – 154 p..

7. Network Science

8. Network Sciense 1st Edition by Albert-Laszlo Barabasi, Marton Postal / Cambridge University Press, 475 p.

1.Stuart J. Russell and Peter Norvig (2022). Artificial Intelligence: A Modern Approach, Global Edition 4th Edition. Pearson Education Limited.

https:// dl.ebooksworld.ir/ books/ Artificial.Intelligence .A.Modern .Approach.Global .Edition.4th. Edition-Peter .Norvig-Stuart .Russell -Pearson -9781292401133 -PDF-E Book sWorld. ir.rar

2. Perry, M.L. (2020). The Art of Immutable Architecture: Theory and Practice of Data Management in Distributed Systems. Apress, Berkeley, CA.

3. Marcio de Queiroz, Xiaoyu Cai, Matthew Feemster. (2019). Formation Control of Multi-Agent Systems:

A Graph Rigidity Approach. JohnWiley & Sons Ltd.

4. Hajduk, M., Sukop, M., Haun, M. (2019). Cognitive Multi-agent Systems. Studies in Systems, Decision and Control, vol 138. Springer, Cham.

5. Research on Intelligent Distributed Systems | Grupo de investigación BISITE | University of Salamanca.

Course materials

1. Formal Specification Using Z, David Lightfoot, Jim Woodcock., 2003 

2. Linz, Peter. An introduction to formal languages and automata / Peter Linz. – 5th ed. p. cm. Includes bibliographical references and index. – ISBN9781449615529.

3. Spivey, J. M. The Z Notation: A Reference Manual. – Prentice Hall International Series in Computer Science, 1992. – 155 р.

4. Jia, X. ZTC: A Type Checker for Z Notation. User’s Guide. – DePaul University. USA: Division of Software Engineering, School of Computer Science, Telecommunication, and Information Systems, 1998, – 44 р.

5. Jacky, J. The Way of Z: Practical Programming with formal Methods. – USA, New York: Cambridge University Press, 1997. – 350 р.

Recommended reading

6. Omelchuk L.L. Formal methods of program specification. 

7. Spivey, J. M. The Z Notation: A Reference Manual. – Prentice Hall International Series in Computer Science, 1992. – 155 с.

8. Jia, X. ZTC: A Type Checker for Z Notation. User’s Guide. – DePaul University. USA: Division of Software Engineering, School of Computer Science, Telecommunication, and Information Systems, 1998, – 44 с.

9. Jacky, J. The Way of Z: Practical Programming with formal Methods. – USA, New York: Cambridge University Press, 1997. – 350 с.

10. УкрІНТЕІ, 2010. – 78 с.

11. Anne Haxthausen. Lecture Notes on The RAISE Development Method. – UNU/IIST, 1999. – 20 с.

12. Chris George. Introduction to RAISE. UNU-IIST report No. 249.– UNU/IIST, 2002. – 128 с.

Internet resources

13. http://www. cyb.univ. kiev.ua/ library /books /omelchuk- 10.pdf

14. https:// web.archive.org /web/ 20150930020243 /http:// www.programs factory.un iv.kie v.ua/r u/co ntent/ books/2/75

Course materials

1. Erl Thomas Service-Oriented Architecture: Analysis and Design for Services and Microservices

Published by Pearson, 2016. – ISBN-10 0133858588. – ISBN 13 9780133858587.

https:// www.abebooks.com /servlet/ BookDetailsPL?

2. Saurabh Shrivastava, Neelanjali Srivastav, Solutions Architect’s Handbook: Kick-start your career as a solutions architect by learning architecture design principles and strategies, 2nd Edition, Packt Publishing, 2022. – 588 p. – ISBN-10 1801816611. – ISBN-13 978-1801816618.

3. James Gough, Matthew Auburn, Daniel Bryant, Mastering API Architecture: Design, Operate, and Evolve API-Based Systems 1st Edition, O’Reilly, 2022. – 286 p. – ISBN 978-1492090632.

4. Dr. Faisal Kaleem, Best Prep for SOA P Exam, 2021. – 758 p. – ISBN-13 979-8544185871.

5. Roberto Vitillo Understanding Distributed Systems, Second Edition: What every developer should know about large distributed applications, 2022. – 344 p. – ISBN-10 1838430210.

https://a.co/d/5r8dMSp

6.J.R. Storment, Mike Fuller Cloud FinOps: Collaborative, Real-Time Cloud Value Decision Making 2nd Edition, 2023. – 454 p. – ISBN-10 1492098353.

https://a.co/d/g3NNeyU

Course materials

1. Podbutskaya, N. V. Psychological foundations of engineering professionalism: structure, dynamics, and patterns of development: [monograph] / N. V. Podbutskaya; Ministry of Education and Science of Ukraine, National Technical University “Kharkiv Polytechnic Institute.” – Kharkiv: Disa Plus, 2016. – 295 p.

2. Conflict communication in engineering: textbook / O. G. Romanovsky, O. S. Ponomarev, N. V. Pidbutskaya; National Technical University “Kharkiv Polytechnic Institute.” – Kharkiv: NTU “KPI,” 2013. – 292 p.

3. Kokun, O. M. Psychology of professional development of modern specialists: monograph / Kokun, O. M.; G. S. Kostiuk Institute of Psychology, National Academy of Pedagogical Sciences of Ukraine. – Kyiv: Inform. – Analytic Agency, 2012. – 200 p.

4. Lozhkin, G. V. Psychology of Labor: Textbook for Students of Higher Educational Institutions / Lozhkin, G. V., Volyanyuk, N. Yu., Soltik, O. O. – Khmelnytskyi, 2013. – Pp. 147–153.

5. Orban-Lembrik, Lydia Ernestovna. Psychology of Professional Communication: Monograph / L. E. Orban-Lembrik. – Chernivtsi: Books – XXI, 2019. – 527 p. – ISBN 978-966-2147-64-3.

6. Psychology of work in normal and extreme conditions [Text]: textbook for university students / [M. S. Korolchuk et al.]; Kyiv National University of Trade and Economics. – Kyiv: KNUTE, 2015. – 651 p. : fig., tables. – Bibliography: p. 601-612. – 150 copies. – ISBN 978-966-629-729-0.

Course materials

1. Gorokhovatsky, V. O. Methods of intellectual analysis and data processing / V. O. Gorokhovatsky, I. S. Tvoroshenko. – Kharkiv: KNURE, 2021. – 90 p.

2. Grus J. Data Science from Scratch: First Principles with Python. 2nd Edition / J. Grus. – O’Reilly, 2019. – 513 p.

3. Gutman A. J. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning / A. J. Gutman, J. Goldmeier. – NY: Wiley, 2021. – 272 p.

4. Reis J. Fundamentals of Data Engineering / J. Reis, M. Housley. – O’Reilly Media, Inc., 2022. – 446 p.

5. Shmueli G. Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro, 2nd Edition / G. Shmueli, P. C. Bruce, M. L. Stephens, M. Anandamurthy, N. R. Patel. – Wiley, 2023. – 608 p.

6. Zaki M. J. Data Mining and Machine Learning: Fundamental Concepts and Algorithms / M. J. Zaki, W. Meira, Jr. – Cambridge University Press, 2020. – 660 p.

Recommended reading

7. MATLAB Text Analytics Toolbox. User’s Guide. – Natick: Mathworks, 2023. – 336 p.

8. MATLAB Statistics and Machine Learning Toolbox. User’s Guide. – Natick: Mathworks, 2023. – 11098 p.

9. MATLAB Fuzzy Logic Toolbox. User’s Guide. – Natick: Mathworks, 2023. – 968 p.

Course materials

1. Methods of data analysis: a textbook for students / Bakhrushin V. E. – Zaporizhia: KPU, 2011. – 268 p.

2. Chernyak, O. I. Intellectual Data Analysis: Textbook. – Kyiv: Znannya, 2014. – 599 p.

3. Ivanov S.M., Maksishko N.K., Brechko D.O. Intellectual data analysis: lecture notes/ S.M. Ivanov, N.K. Maksishko, D.O. Brechko, – Zaporizhia: ZNU, 2020. – 156 p.

4. Kretowski, M. Evolutionary Decision Trees in Large-Scale Data Mining /M. Kretowski, – Cham: Springer International Publishing, June 2019, 180 p. – ISBN 978-3-030-21851-5.

5. Ranga Suri N.N.R., Murty N., Athithan M.G. Outlier Detection: Techniques and Applications. A Data Mining Perspective / N.N.R. Ranga Suri, N. Murty, M.G. Athithan.,- Cham: Springer International Publishing, 2019, 214 p. – ISBN 978-3-030-05127-3

6. Samanta D., Banerjee A. Computationally Intensive Statistics for Intelligent/ D. Samanta, A.Banerjee ,  Springer, 2021, 218 p

Recommended reading

7. A. Burkov. Machine Learning Engineering. – True Positive Inc., 2020. – 310 p.  ISBN 978-199-957-957-9

8. M. Kubat. An Introduction to Machine Learning. – Springer Cham, 2021. – 458 p.  ISBN 978-303-081-935-4

9. S.T. Wierzchoń, M.A. Kłopotek. Modern Algorithms of Cluster Analysis  Springer, Cham, 2018.  421 p.  ISBN 978-3-319-69308-8.

10. Sakarkar Gaurav, Patil Gaurav, Dutta Preteek Machine Learning Algorithms Using Python Programming / Gaurav Sakarkar, Gaurav Patil, Preteek Dutta , – Nova Science Publishers. Inc., June 2021, 170 p.  ISBN 978-1-53619-512-5.

11. Srinivas M., Sucharitha G., Matta A., Chatterjee P. / M. Srinivas, G. Sucharitha, A. Matta, P. Chatterjee Machine Learning Algorithms and Applications: Theory and Applications,-Wiley-Scrivener Publishing, August 2021, 368 p.  ISBN 978-1-119-76924-8.

Internet resources

12. https:// www.coursera .org/ professional- certificates/ ibm-data- science

13. https:// www.coursera .org/ specializations/ applied- data- science

Course materials

1. Kononova K. Yu. Machine Learning. – Kharkiv: V. N. Karazin Kharkiv National University, 2020. – 301 p.

2. R.N. Rao. Machine Learning in Data Science Using Python. – Dreamtech Press, 2022. – 956 p. – ISBN 978-939-154-046-3.

3. P. Chatterjee, M. Yazdani, F. Fernández-Navarro, J. Pérez-Rodríguez. Machine Learning Algorithms and Applications in Engineering. – New York: Taylor & Francis, 2023. – 314 p. – ISBN 978-036-756-912-9.

Recommended reading

4. A. Burkov. Machine Learning Engineering. – True Positive Inc., 2020. – 310 p. – ISBN 978-199-957-957-9.

5. M. Kubat. An Introduction to Machine Learning. – Springer Cham, 2021. – 458 p. – ISBN 978-303-081-935-4

Internet resources

6. https:// en.wikipedia.org/ wiki/ Machine_learning

Course materials

1. Кононова К. Ю. Машинне навчання. – Харків: ХНУ ім. В. Н. Каразіна, 2020. – 301 с.

2. R.N. Rao. Machine Learning in Data Science Using Python. – Dreamtech Press, 2022. – 956 p. – ISBN 978-939-154-046-3.

3. P. Chatterjee, M. Yazdani, F. Fernández-Navarro, J. Pérez-Rodríguez. Machine Learning Algorithms and Applications in Engineering. – New York: Taylor & Francis, 2023. – 314 p. – ISBN 978-036-756-912-9.

Recommended reading

4. A. Burkov. Machine Learning Engineering. – True Positive Inc., 2020. – 310 p. – ISBN 978-199-957-957-9.

5. M. Kubat. An Introduction to Machine Learning. – Springer Cham, 2021. – 458 p. – ISBN 978-303-081-935-4.

Internet resources

6. https://en.wikipedia.org/ wiki/ Machine learning

Course materials

1. Glover F. and Kochenberger G., eds. Handbook of Metaheuristics. – Norwell: Kluwer Academic Publishers, 2002.

2. P. Agrawal, H. F. Abutarboush, T. Ganesh and A. W. Mohamed, “Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019),” in IEEE Access, vol. 9, pp. 26766-26791, 2021.

3. Agrawal, P., Abutarboush, H. F., Ganesh, T., & Mohamed, A. W. (2021). Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019). IEEE Access, 9, 26766–26791.

4. Dorigo M. and St ̈utzle T. Ant Colony Optimization: Overview and Recent Advances // M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, chapter 8, pages 227-263. – New York: Springer, 2010.

5. Dehghani, M., Trojovská, E., & Trojovský, P. (2022). A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process.  Scientific Reports , 12(1), 9924.

6. Ahmad, S. (2022). Electromagnetic Field Optimization Based Selective Harmonic Elimination in a Cascaded Symmetric H-Bridge Inverter. Energies, 15 (20), 7682.

Recommended reading

7. Introduction to genetic algorithms in machine learning.

8. Dynamic programming, greedy algorithms.

Internet resources

9. Electronic resource.

10. Vinicius Fulber-Garcia, Heuristics vs. Meta-Heuristics vs. Probabilistic Algorithms, May 2023