Course detail
Information Representation and Machine Learning
FEKT-DPA-IMLAcad. year: 2021/2022
Complexity theory, genetic algorithms, genetic programming, graph theory, graph equivalence, inforamtion representation, neural networks, reinforcement learning, embeddings.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Offered to foreign students
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
1. Information representation, introduction
2. Complexity theory, selected examples of complexity
3. Graph theory, analysis, factorization
4. Theory of graphs, groups, availability, bipartite
5. Graphs equivalence
6. Information representation - machine learning
7. Information representation - network types
8. Information representation - linear regression
9. Information representation - logistic regression, classification
10. Information representation - feed forward neural network
11, Information representation - recurrent neural network
12. Information representation - reinforcement learning
13. Information representation - NN with graphs and trees
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (EN)
Elearning
Classification of course in study plans
Type of course unit
Elearning