Course detail

Machine Learning

FEKT-NSTUAcad. year: 2019/2020

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. The goal of the subject is to present the key algorithms and theory that form the core of machine learning. Machine learning is mathematical-logical base in many fields including artificial intelligence, pattern recognition or data mining. The main attention is given on classification and optimization tasks.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Karel Horák, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

The graduate is able to
- design own solution of a classification task
- pre-process data, including feature selection
- estimate quality of selected model
- justify rightness of suggested solution
- design own solution of optimization task
- select appropriate search heuristic for given problem

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested namely in mathematics, statistics and probability theory.

Co-requisites

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

Course-unit credit (project): 20 points
Examination: 80 points

Course curriculum

1. Machine learning paradigms. Terminology. Concept learning. Basics of information theory.
2. Statistics in machine learning.
3. Instance based learning.
4. Decision trees.
5. Model performance estimation.
6. Loss functions. Pre-processing 1.
7. Pre-processing 2.
8. Genetic algorithms. Differential evolution. Ant colony optimization.
9. Bayesian learning.
10. Linear regression. Discriminant analysis. Support vector machines.
11. Meta learning, ensemble methods.
12. Unsupervised learning.

Work placements

Not applicable.

Aims

The aim are large knowledge in machine learning with emphasise on classification and optimisation tasks.

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

Only registration and submitting of the project are obligatory to qualify for examination.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Mitchell, Tom M. Machine learning. Boston : McGraw-Hill, 1997. 414 s. McGraw-Hill series in computer science. ISBN 0-07-042807-7. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EECC-MN Master's

    branch MN-KAM , 2 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Syllabus

Machine learning as the integration of artificial intelligence and cognive sciences. Computational processes that are related to learning. Selection of learning algorithms.
Training and testing data. Solution space. Learning and searching.
Natural and human learning. Problem representation language. Learning algorithms with numerical and symbolic inputs.
Methods of decision-tree induction. Presence of noise, incomplete description of examples.
Tree-to-rules transformation, generation of rules.
Perceptrons. Logical neural networks.
Kohonen maps.
Genetic algorithms, genetic programming. Comparision with biological systems.
Pattern recognition. Generalization. Nearest-neghbor method (k-NN).
Instance-based learning (IBL algorithms).
Bayesian classifiers.
Reinforcement learning.
Description and demonstration of applications.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Syllabus

Introduction, safety training.
Introduction to decision trees and program See5 for decision trees induction.
Practical training on program See5.
Introduction to genetic algorithms. GA toolbox for Matlab.
Practical use of GA.
Introduction to IBL methods for text recognition.
Work on individual projects.
Individual work.
Individual work.
Commitment of individual work.
Presentation of individual work.
Evaluation.