13D081SM - Stochastic modeling

Course specification
Course title Stochastic modeling
Acronym 13D081SM
Study programme Electrical Engineering and Computing
Type of study doctoral studies
Lecturer (for classes)
    Lecturer/Associate (for practice)
      Lecturer/Associate (for OTC)
        ESPB 9.0 Status elective
        Condition Knowledge of Mathematics on the level of mandatory courses on undergraduate studies. Probability and Statistics on the level of one semester course.
        The goal Stochastic modeling is a wide area, not only by various applications in different fields, but also by richness of mathematics that it uses. The goal of this course is to enable a student for research in the topic of his/her dissertation.Hence,the ultimate purpose of this course is to make a student familiar with some of numerous models and to learn to use the tools of stochastic modeling.
        The outcome The student will be able to to read and understand scientific literature related to the relevant stochastic models, as well as to apply the acquired knowledge for model making and testing based on data.
        Contents of lectures Conditional distributions. Linear regression. Logistic regression and classification. Monte Carlo methods. Random processes. Poisson process, Brownian process. Continuation (A or B) A. Martingales and stochastic differential equations. B. Robust methods for high-dimensional data.
        Contents of exercises Study research work
        1. Milan Merkle: Verovatnoća i statistika za inženjere i studente tehnike, 4. izmenjeno i dopunjeno izdanje, Akademska misao 2016. (Original title)
        2. J. Michael Steele: Stochastic Calculus and Financial Applications, Springer 2001 (Original title)
        3. Peter Guttorp: Stochastic Modelling of Scientific Data, Chapman&Hall, 1995. (Original title)
        4. Trevor Hastie, Robert Tibshirani, Jerome Friedman: The elements of statistical learning- Data Mining, Inference and Prediction, Second edition, Springer 2017. (Original title)
        Number of hours per week during the semester/trimester/year
        Lectures Exercises OTC Study and Research Other classes
        Methods of teaching Classical teaching, consultations.
        Knowledge score (maximum points 100)
        Pre obligations Points Final exam Points
        Activites during lectures 0 Test paper 40
        Practical lessons 30 Oral examination
        Colloquia 0
        Seminars 30