Objectives and competences
The student will learn methods of intelligent system design, implementation and use focused on modern machine learning techniques.
Content (Syllabus outline)
• Concepts of intelligent system design.
• Applications of IS
• Implementation of IS components in modern tools (e.g. ‘R’)
• Data acquisition for IS
• Advanced knowledge representations and machine learning techniques:
o Symbol-based methods,
o Connectivist methods,
o Association rules,
o Bayes classifiers,
o Hybrid methods,
• Transition from classic connectivist methods to deep learning
• Upgrading knowledge models
• Prediction in dynamic systems and chaos theory.
• Cellular automata.
• Nature-based intelligent systems.
• Evaluation, ethical questions and challenges.
• Functional safety for IS
Learning and teaching methods
• lectures,
• lab work.
Intended learning outcomes - knowledge and understanding
• Present the knowledge of advanced techniques of intelligent system design, implementation and evaluation,
• Understane the safety concept in intelligent systems,
• Use computer tools for data preparation,
• Analyse the results of use of intelligent systems,
• Use the knowledge of intelligent systems for more efficient problem solving
Intended learning outcomes - transferable/key skills and other attributes
Use of information technology: implementation of intelligent systems.
Problem solving: the design and implementation of research studies.
Readings
• I. H. Witten, E. Frank, M.A. Hall: Data Mining, Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Burlington, 2011.
• M. Zorman, et al: Inteligentni sistemi in profesionalni vsakdan, Univerza v Mariboru, Center za interdisciplinarne in multidisciplinarne raziskave in študije, Maribor, 2003.
• J. Han, M. Kamber, J. Pei: Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, 2012.
Prerequisits
Recommended are Basic skills in machine learning and artificial intelligence.
Additional information on implementation and assessment The exam may be replaced by written midterm examinations in the weight of 50%.
