Bi5440 An introduction into modern ICT in biology

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
Mgr. Tomáš Hudík (seminar tutor)
Guaranteed by
doc. Ing. Jan Žižka, CSc.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Standard and alternative data-processing, searching, classification and categorization, regression. The necessity to obtain information and knowledge from data. Information and knowledge representation. Artificial intelligence, machine learning - principles of alternative methods, reasons of their introduction, advantages and disadvantages. Principles of looking for solutions of specific problems, inspiration by intelligent biological systems and their imitation. Problem description, relevant and irrelevant attributes, collecting of suitable data. Application areas, the relationship between modern information technologies and their utilization. Learning and adaptation. Basic algorithms, their time- and space-complexity. Searching in trees, logical systems, decision trees, rules and expert systems, methods of looking for similarity, the nearest-neighbor algorithm, probability systems and the Bayes method, typical iterative and gradient methods, data-driven setting of parameters. Contemporary available methods, possibilities of their practical applications, advantages and disadvantages, correct interpretation and validation of results. Computer experiments with artificial and real data using the WEKA system.
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, T. (1997) Machine Learning. McGraw-Hill.
  • Russel, S. and Norvig, P. (2003) Artificial Intelligence: A modern approach. Second Edition. Prentice Hall.
  • Baldi, P. and Brunak, S. (2001) Bioinformatics: The machine learning approach. Second Edition. The MIT Press.
  • Witten, I. H. and Frank, E. (2005) Data mining: Practical machine learning tools and techniques. Second Edition. Morgan Kaufmann Publishers.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Spring 2021, Spring 2022.