FI:PA034 Machine Learning - Course Information
PA034 Machine Learning
Faculty of InformaticsAutumn 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.
- Enrolment Statistics (Autumn 2005, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2005/PA034