Studies > Machine Learning Systems (IST)

Machine Learning Systems (IST)

Type: Normative

Department: system design

Curriculum

SemesterCreditsReporting
53.5Setoff

Lectures

SemesterAmount of hoursLecturerGroup(s)
532Parubochyi V. O.ФеС-31, ФеС-32

Laboratory works

SemesterAmount of hoursGroupTeacher(s)
532ФеС-31Parubochyi V. O., Lozynskyy V. M.
ФеС-32Lozynskyy V. M., Lozynskyy V. M.

Опис навчальної дисципліни

The course “Machine Learning Systems” examines the basic concepts, goals and problems of machine learning, such as regression, classification, dimensionality reduction, clustering and prediction, and data processing approaches that can be used to solve the problems. The main attention is paid to the assimilation of knowledge and the acquisition of skills corresponding to the current state of development of machine learning, the ability to apply the acquired knowledge practically.

The study subject of the educational discipline is the field of machine learning, its concepts, problems, methods and tools for solving machine learning problems.

A laboratory course is provided to consolidate theoretical knowledge.

Recommended Literature

  1. A. C. Muller and S. Guido, Introduction to Machine Learning with Python, First Edition. Sebastopol, CA: O’Reilly Media, Inc., 2016, ISBN: 978-1-449-36941-5.
  2. F. Chollet, Deep Learning with Python, Second edition. Shelter Island, NY, USA: Manning Publications Co., 2021, ISBN: 978-1-61729-686-4.
  3. A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Third edition. Sebastopol, CA: O’Reilly Media, Inc., 2023, ISBN: 978-1-098-12597-4.
  4. I. Goodfellow, Y. Bengio, and A. Couville, Deep Learning. Cambridge, MA, USA: The MIT Press, 2016. ISBN: 978-0-262-03561-3. Available: https://www.deeplearningbook.org
  5. K. P. Murphy, Probabilistic Machine Learning: An Introduction. Cambridge, MA, USA: The MIT Press, 2022. ISBN: 978-0-262-36930-5. Available: https://probml.github.io/pml-book/book1.html
  6. J. VanderPlas, Python Data Science Handbook, Second Edition. Sebastopol, CA: O’Reilly Media, Inc., 2023. ISBN: 978-1-098-12122-8.
  7. X. Zhu, “Semi-Supervised Learning Literature Survey,” Department of Computer Sciences, University of Wisconsin-Madison, WI, USA, Tech. Rep. TR 1530, Jul. 19, 2008. [Online]. Available: https://pages.cs.wisc.edu/~jerryzhu/research/ssl/semireview.html
  8. X. Zhu and A. B. Goldberg, Introduction to Semi-Supervised Learning. Series Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2009. ISBN: 978-1-598-29547-4, doi: 10.1007/978-3-031-01548-9.
  9. G. Zhong and K. Huang, Semi-Supervised Learning: Background, Applications and Future Directions. Nova Science Pub Inc., 2018. ISBN: 978-1-53613-556-5.
  10. C. Piech, “K Means,” Based on a handout by Andrew Ng, CS 221, Stanford University. [Online]. Available: https://stanford.edu/~cpiech/cs221/handouts/kmeans.html
  11. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd Edition. Melbourne, AU: OTexts, 2021. ISBN: 978-0-987-50713-6. Available: https://otexts.com/fpp3/
  12. A. Amidi and S. Amidi, “CS 229 – Machine Learning cheatsheets,” Stanford University. [Online]. Available: https://stanford.edu/~shervine/teaching/cs-229/
  13. K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: The MIT Press, 2012. ISBN: 978-0-262-01802-9. Available: https://probml.github.io/pml-book/book0.html
  14. K. P. Murphy, Probabilistic Machine Learning: Advanced Topics. Cambridge, MA, USA: The MIT Press, 2023. ISBN: 978-0-262-04843-9. Available: https://probml.github.io/pml-book/book2.html