Artificial Intelligence and Neural Networks in Applications
Code  Completion  Credits  Range  Language 

2371076  Z,ZK  5  2P+2C  Czech 
 Garant předmětu:
 Lecturer:
 Tutor:
 Supervisor:
 Department of Instrumentation and Control Engineering
 Synopsis:

Students will learn about basic problems in the field of artificial intelligence and methods of solving them. The content of the course is: State space, its search methods and their complexity; Genetic algorithms; Basic machine learning algorithms; Clustering; Learning from classified data; Combination of classifiers; Fundamentals of formal propositional and predicate logic as problem solving tools; Automatic theorem proving  resolution method; Neural networks (MLP, CNN, RNN, LSTM), Deep learning.
 Requirements:

Exam question outlines
1. State space and methods for its complete search. A* algorithm and its properties.
2. Use of the state space and its algorithms in problem solving and action planning.
3. Machine learning. Types of machine learning tasks.
4. Basic clustering algorithms and their practical applications.
5. Basic algorithm for decision tree construction and its application.
6. Propositional logic, its syntax, semantics and the notion of logical consequence.
7. Proof means of propositional logic  resolution and its properties.
8. Application of propositional logic in practical knowledge work tasks.
9. Neural networks, basic principles of perceptron, loss function, back propagation, MLP.
10. Convolutional neural networks  principle of convolution, architecture and typical operations, applications.
11. Genetic algorithms  basic concepts (population, objective function, GA cycle). Comparison of evolutionary and swarm algorithms.
 Syllabus of lectures:

1. What is the purpose of AI, what AI can do now and what impact it has on society.
2. State space and methods for solving typical problems.
3. State space  search complexity and how to face it.
4. Genetic algorithms 1
5. Genetic algorithms 2
6. Machine learning and its basic algorithms. Clustering.
7. Learning from classified data. Combination of classifiers.
8. Problem solving theory and the use of formal logic.
9. Propositional and predicate logic
10. Automatic theorem provingresolution method
11. Neural networks, theories, perceptron, MLP
12. Deep learning, convolutional neural networks, influence of architecture
13. Neural networks for natural language processing, RNN, LSTM; Transformers.
 Syllabus of tutorials:

The topics of the seminaries follow the topics of lectures.
Conditions for the assessment:
Credit Conditions:
 Active Participation: Attend at least 70% of the labs.
 Submission of Assignments: Submit 3 out of 5 assigned individual until the deadline 14 days from the beginning of the examination period.
 During the semester, a total of 5 individual tasks from various topics covered in the curriculum will be assigned during the labs.
 Students have 14 days to submit each task. If an assignment is submitted on time, the student can earn the maximum number of points for that task.
 The maximum points decrease by 1 point per day of delay beyond the submission deadline, until it reaches 0 points.
 These earned points contribute to the overall evaluation for the final exam.
 Study Objective:
 Study materials:

1. Russel, Stuart and Norvig, Peter (2022 – the 4th edition) (parts of chapters 2, 3, 6, 7, 10, 18). Artificial Intelligence: A Modern Approach (Prentice Hall, 1995 – the 1st edition), ISBN 9780134610993.
2. Mitchell, Melanie (1996). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. ISBN 9780585030944
3. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning. MIT Press, 2016. [online] Available: https://www.deeplearningbook.org/
 Note:
 Further information:
 No timetable has been prepared for this course
 The course is a part of the following study plans: