PA034 Machine Learning

Faculty of Informatics
Autumn 2005
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
Guaranteed by
prof. RNDr. Jiří Hřebíček, CSc.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: doc. Ing. Jan Žižka, CSc.
Timetable
Wed 16:00–17:50 B204 and each odd Wednesday 18:00–19:50 B116
Prerequisites (in Czech)
! P034 Machine Learning
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
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.
Syllabus
  • Machine learning as the integration of artificial intelligence and cognitive sciences. Computational processes that are related to learning. Selection of learning algorithms.
  • Training and testing data. Learning and searching. Natural and human learning. Problem representation language. Learning algorithms with numerical and symbolic inputs.
  • Decision-tree induction. Presence of noise, incomplete description of examples. Tree-to-rules transformation. Bagging, boosting.
  • Perceptrons. Logical neural networks. Kohonen maps. Genetic algorithms, genetic programming. Comparision with biological systems.
  • Pattern recognition. Generalization. Nearest-neighbor method (k-NN). Instance-based learning (IBL algorithms).
  • Bayesian classifiers.
  • SVM (Support Vector Machines).
  • Description and demonstration of applications.
Literature
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2006.
  • Enrolment Statistics (Autumn 2005, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2005/PA034