Course detail

Artificial Intelligence

FEKT-BPC-UINAcad. year: 2018/2019

The course discusses the basic methods and subdomains of artificial intelligence, namely, machine learning, the structure and activity of knowledge systems, optical information processing, and approaches to the training and application of artificial neural networks.

Learning outcomes of the course unit

Course graduate should be able to:
- explain the concept of artificial intelligence from the perspective of its application in technical equipment,
- explain the paradigm for artificial neural network: perceptron, multilayer neural network backpropagation learning, Kohonen self-organizing maps, Hopfield network, RCE neural network,
- discuss and verify the settings of individual parameters of the selected neural network,
- assess the scope of application of artificial neural network,
- explain the architecture and functionality of knowledge systéme,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systéme,
- optical information processing devices applied artificial inteligence.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratories. Students have to write a single project during the course.

Assesment methods and criteria linked to learning outcomes

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons - the computer exercises and obtaining at least 15 points. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. Artificial intelligence: history, definition, and subdomains
2. Intelligence: the biological information system; neuron; brain; data; information; knowledge
3. Machine learning: the basic concepts and methods
4. Problem solving and knowledge representation: introduction and fundamental techniques
5. Knowledge-based systems: the structure and activity of expert systems
6. Artificial neural networks: the perceptron; backpropagation learning algorithm; convolutional neural networks
7. Computer vision

Aims

The course aims to explain the basic concepts (algorithms) of artificial intelligence, with special emphasis on machine learning, problem solving, knowledge representation, knowledge systems, computer vision, and artificial neural networks.

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

The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.

Classification of course in study plans

  • Programme BPC-AMT Bachelor's, 3. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

eLearning