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Main menu for Browse IS/STAG
Course info
KMA / ZTI
:
Course description
Department/Unit / Abbreviation
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KMA
/
ZTI
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Fundamentals of Information Theory
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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|
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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6 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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KIV/ZTI
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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KMA/TIS
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The main objective is to obtain basic knowledge of information theory with regard to use in economic and investment modeling.
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Requirements on student
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Knowledge and understanding of lectures and tutorials. Ability to apply it to examples.
Upon repeated registration of the course, the credit obtained in the previous study of this course is not recognized.
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Content
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Finite-state machine - basic concepts.
Finite-state machine operations, non-deterministic finite-state machine.
Entropy, relative entropy, mutual information.
Typical sequences, experiment planning and evaluation, data compression.
Error-correcting code, connection with questionnaire construction.
Channel capacity, classification as a communication channel.
Gambling and connection with information theory.
Gambling and portfolio theory.
Queuing theory - basic concepts.
Queuing theory - basic models.
Information theory and statistics.
Maximum entropy principle and its use in estimation.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for an examination (30-60)
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60
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Preparation for comprehensive test (10-40)
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20
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Preparation for formative assessments (2-20)
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20
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Contact hours
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65
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Total
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165
|
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
formulovat a vysvětlit základy teorie pravděpodobnosti (v rozsahu předmětu KMA/PSA) |
formulovat a vysvětlit principy testování statistických hypotéz (v rozsahu předmětu KMA/PSA) |
formulovat a vysvětlit základní pojmy diferenciálního a integrálního počtu (v rozsahu předmětů KMA/M1 a KMA/M2) |
formulovat a vysvětlit základní operace maticového počtu (v rozsahu předmětu KMA/LAA) |
formulovat a vysvětlit základní pojmy z finanční matematiky, zejména v oblasti investičního rozhodování (v rozsahu předmětu KMA/FIPM) |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
řešit úlohy pomocí tabulkového procesoru, tj. použít kontingenční tabulky, vzorce, absolutní a relativní odkazy a případná rozšíření, jako např. řešitele v MS Excel |
využít diferenciálního, integrálního a maticového počtu při řešení úloh |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
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Learning outcomes
|
Knowledge - knowledge resulting from the course: |
popsat deterministické, nedeterministické a pravděpodobnostní automaty a jejich využití v oblasti systémů hromadné obsluhy |
definovat pojem entropie a popsat její užití v oblasti kódování, kapacity kanálu, návrhu a vyhodnocování experimentů, klasifikace, teorie sázek a investování a statistice |
popsat vybrané metody komprese dat a bezpečnostních kódů |
Skills - skills resulting from the course: |
použít teorii automatů pro vytvoření abstraktního modelu problému |
aplikovat entropii pro vyhodnocování a plánování experimentů |
použít metody komprese dat a bezpečnostních kódů |
aplikovat teorii informace v oblasti teorie sázek a portfolia |
použít základní modely systémů hromadné obsluhy |
využít teorii informace v oblasti statistiky |
Competences - competences resulting from the course: |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Test |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Test |
Combined exam |
Competences - competence achieved by taking this course are verified by the following means: |
Test |
Combined exam |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture with visual aids |
Interactive lecture |
Individual study |
Self-study of literature |
Skills - the following training methods are used to achieve the required skills: |
Lecture with visual aids |
Interactive lecture |
Individual study |
Self-study of literature |
Competences - the following training methods are used to achieve the required competences: |
Lecture with visual aids |
Interactive lecture |
Individual study |
Self-study of literature |
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