Course detail
Machine Learning
FEKT-LSTUAcad. year: 2012/2013
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 interdisciplinary, draws on concepts and results from many fields, including statistics, artificial intelligence, information theory, philosophy, biology, cognitive science, and control theory.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Examination - 80 points
Course curriculum
2. Statistics in machine learning.
3. Instance based learning.
4. Decision trees.
5. Model performance estimation.
6. Loss functions. Preprocessing.
7. Genetic algorithms. Ant colony optimization.
8. Bayesian learning.
9. Linear regression. Discriminant analysis. Support vector machine.
10. Data mining.
11. Meta learning.
12. Unsupervised learning.
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
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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.