Course description
Intelligence of natural and artificial systems: historical definition of intelligence and overview of the development of intelligent systems. User interaction with Information-Communication Technologies. Intelligent systems modeling and prototyping of socio-technical systems. The use of intelligence in information and communication systems: user interfaces, intelligent terminals, ubiquity, identification, user modelling, data mining, personalization. Methods and algorithms of intelligent systems and machine learning algorithms. Knowledge analysis and modelling, methods of learning. Building intelligent systems: data acquisition, data processing, and system’s response. Methods for model validation.
Course is carried out on study programme
Electrical engineering 1st level
Objectives and competences
Understanding intelligence in modern information and communication systems in relation to the user. Practical use of tools and techniques to support modeling, decision-making and in the management of information. Familiarity, understanding and ability to use appropriate machine learning algorithms.
Learning and teaching methods
The lectures provide a theoretical background on selected topics together with simple practical demonstrations. A complete study material is available online.
Practical work is being performed in the laboratory environment. Individual projects are based on selected topics and presented by students.
Intended learning outcomes
After successful completion of the course, students should be able to:
Understand the intelligence in modern ICT systems.
Understand and use basics of modeling interactive communication processes.
Develop a prototype of a Internet of things system with NodeRed.
Use of modern simulation tools for machine learning.
Solve machine learning problems with a set of selected methods.
Understand and be proficient in applying and using machine learning model performance metrics.
Reference nosilca
- KOLENC, Mitja, NEMČEK, Peter, GUTSCHI, Christoph, SULJANOVIĆ, Nermin, ZAJC, Matej. Performance evaluation of a virtual power plant communication system providing ancillary services. Electric power systems research. [Print ed.]. Aug. 2017, vol. 149, str. 46-54, ilustr. ISSN 0378-7796.
- KOLENC, Mitja, IHLE, Norman, GUTSCHI, Christoph, NEMČEK, Peter, BREITKREUZ, Thomas, GÖDDERZ, Karlheinz, SULJANOVIĆ, Nermin, ZAJC, Matej. Virtual power plant architecture using OpenADR 2.0b for dynamic charging of automated guided vehicles. International journal of electrical power & energy systems. [Print ed.]. Jan. 2019, vol. 104, str. 370-382, ilustr. ISSN 0142-0615.
- MILEV, Ivana, PRISLAN, Lev, ZAJC, Matej. Energy portal design and evaluation for consumer active participation in energy services : seven-month field study with 234 Slovenian households. Electronics. Nov.-1 2022, iss. 21, 3452, str. 1-20, ilustr. ISSN 2079-9292.
- MEŽA, Marko, KOŠIR, Janja, STRLE, Gregor, KOŠIR, Andrej. Towards automatic real-time estimation of observed learner's attention using psychophysiological and affective signals : the touch-typing study case. IEEE access, doi: 10.1109/ACCESS.2017.2750758.
- 5. BERTALANIČ, Blaž, MEŽA, Marko, FORTUNA, Carolina. Resource-aware time series imaging classification for wireless link layer anomalies. IEEE transactions on neural networks and learning systems. [Print ed.]. [in press] 2022, str. 1-13, ilustr. ISSN 2162-237X. DOI: 10.1109/TNNLS.2022.3149091. [COBISS.SI-ID 98005507]
- 6. KOŠIR, Andrej, STRLE, Gregor, MEŽA, Marko. Weak ground truth determination of continuous human-rated data. IEEE access. 2021, vol. 9, str. 4594-4606, ilustr. ISSN 2169-3536. https://ieeexplore.ieee.org/document/9301304, DOI: 10.1109/ACCESS.2020.3046293. [COBISS.SI-ID 48480771]
Study materials
- Tamboli A. Build Your Own IoT Platform : Develop a Flexible and Scalable Internet of Things Platform. Apress Media LLC, 2022.
- Witten I. H. Data mining : practical machine learning tools and techniques. Morgan Kaufmann, 2017.
- Norris D. Home Automation with Raspberry Pi. McGraw-Hill Education, 2019.