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13S082VS - Probability and Statistics

Course specification
Course title Probability and Statistics
Acronym 13S082VS
Study programme Software Engineering
Module
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition Passed exams in Mathematics 1 and Mathematics 2.
The goal Learning about discrete and continuous stochastic models and their applications, in particular in computer sciences.
The outcome A student will have a basic knowledge about simple and moderately complex discrete and continuous stochastic models and typical applications, particularly in computer sciences. He/she will be able to apply correct models in real life problems, and also to apply corresponding tools and procedures of statistics and probability.
Contents
Contents of lectures Combinatorial models. Conditional probability, independence. Bayes formula. Random variables and vectors. Information and entropy. Laws of large numbers. Central limit theorem. Estimation and hypotheses testing. Conditional distribution and linear regression. Monte Carlo methods.
Contents of exercises Exercises in examples and problem solving. Project asignments in teams, with presentations
Literature
  1. Milan Merkle: Probability and Statistics for engineers and students of engineering, Academic Mind, Belgrade 2010
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
2 2 1
Methods of teaching 30 hours of lectures and 30 hours of exercises in problem solving, 15 hours of discussions related to homework problems and preoject assignments.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 50
Practical lessons 0 Oral examination 0
Projects
Colloquia 30
Seminars 20