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
• R. E. Neapolitan, X. Jiang: Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, CRC, 2018
• Burkov, A. (2019). The hundred-page machine learning book (p. XVIII, 141). Andriy Burkov.
• K.-L. Du, M. N. S. Swamy: Neural Networks and Statistical Learning. Springer, London, 2014
• Spiegel, M. R., & Stephens, L. J. (2008). Schaum’s outline of theory and problems of statistics (4th ed., p. XIX, 577). McGraw-Hill.
Prerequisits
Recommended basic knowledge of programming and mathematics.
