Course detail
Artifical Inteligence
FSI-RAIAcad. year: 2023/2024
The course introduces the essential approaches in artificial intelligence area, including the state space search methods, stochastic optimization and machine learning, in particular the artificial neural networks including the convolution neural networks. Usage of the methods is demonstrated on solving simple engineering problems using corresponding tools (Matlab, TensorFlow).
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Lectures are optional, but are highly recommended. The practices are obligatory. The way the student can substitute its absence is up to the teacher.
Aims
Student will gain the overal knowledge in the area of artificial intelligence methods and will be capable of applying the appropriate methods in solving engineering problems.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
- Programme N-MET-P Master's 1 year of study, summer semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. State space search - introduction.
3. Blind and informed methods of state space search.
4. Game theory – min/max algorithm
5. Evolution methods of state space search.
6. Basic paradigms of neural networks
7. Unsupervised/supervised learning.
8. Backpropagation.
9. Approximation versus classification.
10. Convolution neural networks - intro
11. Convolution neural networks - topology, convolution and pooling layers
12. Reinforcement learning
13. Q-learning
Computer-assisted exercise
Teacher / Lecturer
Syllabus
2. Breadth/depth first search algorithms
3. Dijkstra algorithm, A-star
4. Min-max algorithm
5. Genetic algorithm
6. Layered networks, Neural Network Toolbox
7. Layered networks – examples
8. Convolution neural network – Tensor Flow
9. Reinforcement learning and Q-learning
10. Project, consultations
11. Project, consultations
12. Project, consultations
13. Project presentation
Elearning