Course detail

Artifical Inteligence

FSI-RAIAcad. year: 2020/2021

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).

Learning outcomes of the course unit

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.

Prerequisites

Vector and matrix calculations, algoritmization abilities, ability to implement given algorithm in Matlab and/or Python.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Hope T.: Learning TensorFlow: A Guide to Building Deep Learning Systems, O'Reilly Media, 2017 (EN)
Mařík a kol.: Umělá inteligence (1-6), Academia (CS)

Planned learning activities and teaching methods

The subject is taught using a set of lectures explaining the basic principles and theory of given area. The practical part is focused on actual implementation of explained methods using appropriate tools, such as Matlab or Python.

Assesment methods and criteria linked to learning outcomes

The subject evaluation is based on the implementation of softttware project that uses selected method of artificial intelligence. The project final report has to be delivered including the source code and presented to the audience in the form of short presentation.

Language of instruction

Czech

Work placements

Not applicable.

Aims

Understanding of the basics of artificial intelligence approaches and ability to apply those in solving engineering tasks.

Specification of controlled education, way of implementation and compensation for absences

Lectures are optional, but are highly recommended. The practices are obligatory. The way the student can substitute its absence is up to the teacher.

Classification of course in study plans

  • Programme N-MET-P Master's, 1. year of study, summer semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Introduction, areas of artificial intelligence.
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

26 hours, compulsory

Teacher / Lecturer

Syllabus

1. Essential tools: Matlab, Python, Tensor Flow, Keras.
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