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Objectives and competences

Cilj predmeta je študente usposobiti za uporabo metod strojnega učenja in za presojo rezultatov uporabe strojnega učenja na izbranih primerih s področja njihovega matičnega študijskega programa.

Content (Syllabus outline)

Artificial intelligence: basic concepts and areas of use. Problems that can be solved with machine learning. Machine learning algorithms: supervised learning, unsupervised learning, reinforcement learning. Practical approaches to machine learning: analysis and preparation of data for machine learning, algorithm selection, evaluation measures. Fundamentals of statistics: basic terms, mean values, important statistical distributions, law of large numbers. Statistical tests: null and alternative hypothesis, test statistics, parametric and non-parametric tests.

Learning and teaching methods

• Lectures • Seminar • Individual work

Intended learning outcomes - knowledge and understanding

Knowledge and understanding: • describe the basic principles of frequently used machine learning methods • use existing methods and tools for machine learning on problems from their primary study program • describe basic terms from statistics and statistical hypothesis testing, and select the appropriate statistical test • use the statistical significance test and interpret the results on a machine learning example Transferable/Key skills and other attributes: • manner of oral expression at the presentation of seminar paper • use of information technology

Readings

• Sepesy Maučec Mirjam, Donaj Gregor: Osnove strojnega učenja in statistične presoje rezultatov, interno gradivo, UM FERI, 2023. • R. E. Neapolitan, X. Jiang: Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, CRC, 2018: • Burkov: The Hundred-page Machine Learning Book, Andriy Burkov, 2019. • K.-L. Du, M. N. S. Swamy: Neural Networks and Statistical Learning. Springer, London, 2014. • F. Daly, D.J. Hand, C. Jones, D. Lunn, K. McConway: Elements of Statistics, Addisson-Wesley, 1995 • K. Košmelj: Uporabna statistika. Biotehnična fakulteta, Ljubljana 2001.

Prerequisits

Recommended basic knowledge of programming and mathematics.

  • izr. prof. dr. MIRJAM SEPESY MAUČEC, univ. dipl. inž. rač. in inf.
  • doc. dr. GREGOR DONAJ, univ. dipl. inž. el.

  • Seminar paper: 60
  • Oral examination: 40

  • : 20
  • : 10
  • : 60

  • Slovenian
  • Slovenian