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DS2SOM - Decision Making Systems in Medicine

Course specification
Course title Decision Making Systems in Medicine
Acronym DS2SOM
Study programme Electrical Engineering and Computing
Module System Control and Signal Processing
Type of study doctoral studies
Lecturer (for classes)
    Lecturer/Associate (for practice)
      Lecturer/Associate (for OTC)
        ESPB 9.0 Status elective
        Condition none
        The goal Objective of the course is for the students to be informed about the statistical methods for pattern recognition in terms of medicine: hypothesis testing, parametric and nonparametric classification, clustering.
        The outcome Learning outcomes of the course are following: students´ ability to generate or to collect high quality and informative training sets of data, to apply appropriate statistical pattern recognition technique (hypothesis testing, parametric or nonparametric classifier), to design system for data clustering.
        Contents
        Contents of lectures Probability decision-making methods. Evaluation of competing hypotheses Bayesian analysis. Inductive methods based on the minimization of risk. Decision-making methods based on explicit knowledge. Evaluation decision system.
        Contents of exercises
        Literature
        1. R.O.Duda, P.E.Hart, "Pattern Classification", Second Edition, John Waley & Sons, 2001. (Original title)
        2. T.Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning, Data Mining, Inference, and Prediction", Springer, 2001. (Original title)
        3. C.M.Bishop, "Pattern Recognition and Machine Learning", Springer, 2006. (Original title)
        4. R.P.W. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D.M.J. Tax, PRTools4, A Matlab Toolbox for Pattern Recognition, Delft University of Technology, 2004. (Original title)
        Number of hours per week during the semester/trimester/year
        Lectures Exercises OTC Study and Research Other classes
        6
        Methods of teaching Lectures and auditory exercises.
        Knowledge score (maximum points 100)
        Pre obligations Points Final exam Points
        Activites during lectures 0 Test paper 70
        Practical lessons 0 Oral examination 0
        Projects 0
        Colloquia 30
        Seminars 0