Theory: 50 %

Practice: 50 %

Recommended semester: 5

Study mode: full-time

Prerequisites: 100 cr

Evaluation type: term mark

Course category: required optional

Language: english

Responsible instructor: Dr. Pásztor Attila

Responsible department: Department of Information Technologies

Instructor(s):

Course objectives:
The students should get to know the place of AI among scientific fields, its development, main areas and be able to independently algorithmize and program tasks. Learn about the practical application possibilities of AI.

Course content - lectures:

1. The concept of Artificial Intelligence, its research and application areas. The concept of the problem space and the possibilities of narrowing it down. 2. Illustrating problems with directed graphs: general and special pathfinding problems. 3. Graph representation: search task in graphs. -graphs, AND/OR graphs. 4. Search algorithms. Representation of two-player games, existence of winning strategy. Minimax algorithm and its corrections (e.g. alphabet truncation). 5. Evolutionary algorithms. Concepts of population, fitness function, criterion function. 6. Evolutionary strategies, genetic algorithms. Agents. Properties of agents (ideal rational, autonomous), structure and categories (reflexive, goal-oriented, utility-oriented). 7. Learning agents. The relationship between an agent and its environment. Multi-agent systems. Communication between agents: cooperating and competing agents. 8. Concepts of coordination and cooperation. Swarm intelligence, swarm intelligence in nature. 9. The concept of the robot, the milestones of its development. Robot hardware, sensors and actuators. 10. Sensing in robotics, positioning, mapping. Motion planning: configuration space, cell decomposition, skeletonization method. 11. Structure and classification of robots: manipulators, mobile robots, humanoids, androids. The living spaces and areas of use of robots, nano technology, dynamic grip recognition, brain fingerprinting, the relationship between the animal world and robotics. 12. Detection and navigation. Imaging, image processing tools, 3-D information extraction. 13. Navigation and manipulation using vision. Navigation and motion planning. Speech recognition. Fuzzy logic. Fuzzy inference (Mamdani, Takagi-Sugeno).

Course content - labs:

1. The student can learn about the practical foundations of Artificial Intelligence. 2. These foundations are explored through different types of Microsoft Azure services. 3. Overview of the cloud technologies themselves. 4. The course then continues with a general Microsoft Azure service management primer. 5. Prioritize the capabilities of the prebuilt AI. These capabilities are used through various REST endpoints. 6. First Practical Test 7. We will use the REST API endpoints through individual developments. 8. Additionally, we will utilize Microsoft Azure Logic Apps, a process automation tool. 9. Students can gain insight into the creation of chatbots. 10. The course covers the management of Azure Q&A Maker services. 11. It also includes the management of LUIS services. The last part of the course is focused on the Azure ML service. This service provides an interface suitable for training and deploying models. 12. First Practical Test 13. Retake Test

Acquired competences:
Knowledge:

- He knows the vocabulary and special terms of the engineering profession in the Hungarian and English languages at least on the basic level.

Skills:

He/she can apply his/her knowledge acquired during his/her study to acquire deeper knowledge in the field of information engineering and to process special literature and solve problems related to information technology. - He/she constantly improves his/her knowledge and keeps up with the development of the computer engineering profession.

Attitude:

Autonomy and responsibilities:

- He/she feels responsible for IT systems analysis, development and operation, both individually and as part of a team.

Additional professional competences:

Requirements, evaluation, grading:
Mid-term study requirements:
During the semester, at least 50% of the points can be obtained in the test . The required points can be obtained in two theoretical ZH (60min) and 2 practical test. The material for the test is a set of theoretical and practical questions based on the lectures. The marks offered are based on the marks obtained in the examination.
Exam requirements:

Study aids, laboratory background:

Recommended literature, exercise programmes, lecture material in PPT. Each student will be provided with a separate state-of-the-art computer for the exercises. Internet resources are available in the computer service room for students.

Compulsory readings:

[1] Stuart Russell: Human Compatible: Artificial Intelligence and the Problem of Control, 2019, ISBN 9780525558613 [2] Brad Smith and Harry Shum: The Future Computed. Artificial Intelligence and its role in society, Microsoft, 2018, ISBN 978-0-9997508-1-0 https://blogs.microsoft.com/blog/2018/01/17/future-computed-artificial-intelligence-role-society/

Recommended readings: