13M114IS - Intelligent Services and Systems
Course specification | ||||
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Course title | Intelligent Services and Systems | |||
Acronym | 13M114IS | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | Introducing students to the basic concepts and techniques of artificial intelligence, machine learning, and intelligent systems. During the course, students will study the most popular models for designing, implementing, and testing these types of applications. | |||
The outcome | In this course, students will learn the foundational principles that drive AI applications and practice implementing some of these systems. The main outcome of the course is to equip students with techniques and tools to tackle new AI and machine learning problems that they may encounter in life and to apply the most appropriate and effective method for solving them based on their knowledge. | |||
Contents | ||||
Contents of lectures | Search strategies: algorithms, performance, efficiency, complexity. Game theory algorithms and their application. Production and analytical systems. Planning - problem, and types. Knowledge and reasoning in an uncertain environment. Bayesian networks. Problem-solving strategies. Machine learning: regression, classification, and clustering. | |||
Contents of exercises | Visual simulations of theoretically treated problems. Analyzing and solving practical tasks and demonstrating how to overcome certain problems with artificial intelligence and machine learning techniques. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
2 | 2 | 1 | ||
Methods of teaching | Lectures, auditory exercises, independent preparation of several homework assignments, and laboratory exercises with visual simulations. | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 30 | |
Practical lessons | 0 | Oral examination | 0 | |
Projects | 20 | |||
Colloquia | 50 | |||
Seminars | 0 |