P034 Machine Learning

Faculty of Informatics
Autumn 2001
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. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. Ing. Jan Žižka, CSc.
Timetable
Tue 9:00–10:50 B011
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
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 cognive 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.
  • 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.
Literature
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
Assessment methods (in Czech)
Výuka formou přednášek a cvičení. Zkouška písemná.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Autumn 1995, Autumn 1996, Autumn 1997, Autumn 1998, Autumn 1999, Autumn 2000.
  • Enrolment Statistics (recent)
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