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.
