Studies > Methods and technologies of machine learning (HPC)

Methods and technologies of machine learning (HPC)

Type: Normative

Department: system design

Curriculum

SemesterCreditsReporting
63.5Exam

Lectures

SemesterAmount of hoursLecturerGroup(s)
632Associate Professor Lyashkevych V. Y.

Laboratory works

SemesterAmount of hoursGroupTeacher(s)
632

Опис навчальної дисципліни

The curriculum of the discipline “Methods and technologies of machine learning” determines the content and scope of knowledge necessary for a specialist in intelligent data processing technologies. The discipline covers the issues of studying the current state of machine learning technologies used for formalization and processing of data in systems functioning technologies, studying modern computer data processing software tools, and technologies for their design, implementation, and research. Within the scope of the educational discipline “Methods and Technologies of Machine Learning” students study the methods used to build complex models and algorithms.

Recommended Literature

  1. Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow: O’Reilly, 2017. – 718 p.
  2. Mohri M., Rostamizadeh A., Talwalkar A. Foundations of Machine Learning. MIT Press, 2012. 
  3. Andrew Ng. Machine Learning Yarning. – [Електронний ресурс]. – Режим доступу: https://nessie.ilab.sztaki.hu/~kornai/2020/AdvancedMachineLearning/Ng_MachineLearningYearning.pdf 
  4. Alex Smola, S.V.N. Vishwanathan. Introduction to machine learning: Cambridge University Press, 2008. – 234 p.
  5. Hastie T., Tibshirani R, Friedman J. The Elements of Statistical Learning (2nd edition). Springer, 2009. 
  6. Machine learning. A First Course for Engineers and Scientists / Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön // Cambridge University Press, 2021. – 275 p.
  7. Bishop C. M. Pattern Recognition and Machine Learning. Springer, 2006. 
  8.  Mohammed J. Zaki, Wagner Meira Jr. Data Mining and Analysis. Fundamental Concepts and Algorithms. Cambridge University Press, 2014. 
  9. Charu C. Aggarwal. Recommender Systems: Springer, 2016. – 518 p.
  10. Kishan G. Mehrotra Chilukuri K. Mohan HuaMing Huang. Anomaly Detection Principles and Algorithms: Springer. – 2017. – 229 p. – DOI: https://doi.org/10.1007/978-3-319-67526-8  
  11. Machine Learning in Computer Vision / N. Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang// Springer, 2005. – 249 p. – Режим доступу:

http://silverio.net.br/heitor/disciplinas/eeica/papers/Livros/[Sebe]%20-%20Machine%20Learning%20in%20Computer%20Vision.pdf 

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