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Course info
KKY / SUR
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Course description
Department/Unit / Abbreviation
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KKY
/
SUR
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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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Pattern Recognition and Machine Learning
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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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Yes in the case of a previous evaluation 4 nebo nic.
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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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Yes in the case of a previous evaluation 4 nebo nic.
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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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23 / -
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0 / -
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1 / -
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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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1
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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 |
Yes
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Fundamental course |
No
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Fundamental theoretical course |
Yes
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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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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KKY/ZSUR
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Courses depending on this Course
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KKY/AKSZ, KKY/ROSZ, KKY/SZKUI
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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 goal of the course is to present an overview of fundamental methods of problem solving, machine learning and pattern recognition. Students acquire theoretical and practical knowledge to solve selected problems from the area discussed in the course.
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Requirements on student
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Coming to the exam will be conditioned by elaborating individual task supplemented by written report. The exam will contain both the written and the oral parts.
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Content
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Introduction, problem solving algorithms, optimal and suboptimal search, Astar.
Classification based on Bayes decision theory. Maximum-Likelihood estimation, EM algorithm. Linear discriminant function. SVM (Support Vector Machines) classifier.
Neural and Bayesian networks.
Context dependent classifiers, dynamic programming approach, Hidden Markov models.
Learning with decision trees.
Unsupervised learning and clustering (iterative optimization, hierarchical clustering). Gaussian mixtures, clustering with maximum likelihood.
Feature extraction and selection methods. Principal and Independent Component Analysis.
Conclusion.
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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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-
Extending:
Kotek, Zdeněk, Mařík, Vladimír. Metody rozpoznávání a jejich aplikace. Academia, Praha, 1993. ISBN 80-200-0297-9.
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Extending:
Duda, R.O., Hart, P.E., Stork, D.G. Pattern Classification. Wiley, 2000. ISBN 978-0-471-05669-0.
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Recommended:
Theodoridis, S., Kouroumbas, K. Pattern recognition. Elsevier, 2008. ISBN 978-1-597-49272-0.
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Recommended:
Bishop, C.M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0387-31073-2.
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Recommended:
Mařík V. a kol. Umělá inteligence 1. Academia, Praha, 1993. ISBN 80-200-0496-3.
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On-line library catalogues
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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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Practical training (number of hours)
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10
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Contact hours
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39
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Presentation preparation (report) (1-10)
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10
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Preparation for an examination (30-60)
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60
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Individual project (40)
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40
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Total
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159
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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: |
disponovat základními znalostmi matematické analýzy, lineární algebry, teorie pravděpodobnosti a statistiky |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
aktivně využívat dříve získané znalosti z oblasti matematické analýzy, lineární algebry, matematické pravděpodobnosti a statistiky |
pracovat v programovacím jazyku Matlab |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
řešit problémy náležející do oblasti automatického řešení úloh |
navrhnout jednoduchý klasifikátor založený na Bayesově teorii rozhodování, odhadnout parametry klasifikátoru metodou maximální věrohodnosti (EM algoritmus) |
navrhnout a natrénovat klasifikátor s lineární diskriminační funkcí, SVM klasifikátor |
navrhnout kontextově závislý klasifikátor - navrhnout a natrénovat klasifikátor s částečnou anebou žádnou informací učitele - metody shlukové analýzy |
provést výběr informativních příznaků u metody shlukové analýzy |
Skills - skills resulting from the course: |
student dovede analyticky přemýšlet;
student dovede používat poznatky získané dřívějším studiem v oblasti matematiky, výpočetní techniky a základů kybernetiky |
Competences - competences resulting from the course: |
N/A |
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: |
Combined exam |
Seminar work |
odborné znalosti jsou hodnoceny formou kombinované zkoušky a zpracováním referátů seminárních prací |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
použitelnost odborných dovedností je posuzována během zkoušky a během obhajoby seminárních prací |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
obecné způsobilosti jsou s důrazem na praktičnost sledovány a postupně hodnoceny v průběhu obhajoby seminárních úloh a prováděné zkoušky |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Task-based study method |
Self-study of literature |
Individual study |
Seminar |
Skills - the following training methods are used to achieve the required skills: |
Lecture |
Seminar |
Self-study of literature |
Seminar classes |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
Practicum |
Textual studies |
Self-study of literature |
Students' portfolio |
Individual study |
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