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Artificial Intelligence and Neural Networks in Applications (E371076)

Departments: | ústav přístrojové a řídící techniky (12110) | ||

Abbreviation: | Approved: | 17.01.2012 | |

Valid until: | ?? | Range: | 2P+2C |

Semestr: | * | Credits: | 5 |

Completion: | Z,ZK | Language: | EN |

Annotation

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.

Teacher's

Ing. Cyril Oswald Ph.D.

Zimní 2023/2024

prof. RNDr. Olga Štěpánková CSc.

Zimní 2023/2024

prof. Ing. Jiří Bíla DrSc.

Zimní 2022/2023

Ing. Cyril Oswald Ph.D.

Zimní 2022/2023

prof. Ing. Jiří Bíla DrSc.

Zimní 2021/2022

Ing. Cyril Oswald Ph.D.

Zimní 2021/2022

prof. Ing. Jiří Bíla DrSc.

Zimní 2020/2021

Ing. Cyril Oswald Ph.D.

Zimní 2020/2021

Structure

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 proving-resolution 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.

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 proving-resolution 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.

Structure of tutorial

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.

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.

Literarture

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 978-0134610993.

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/

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/

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.

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.

Keywords

Theory of problem solving, formal logic, formal grammars, fuzzy controllers, genetic algorithms, neural networks.

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