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Main menu for Browse IS/STAG
Course info
KIV / SU-E
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Course description
Department/Unit / Abbreviation
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KIV
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SU-E
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Academic Year
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2024/2025
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Academic Year
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2024/2025
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Title
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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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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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English
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Occ/max
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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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0 / -
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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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English
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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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None
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Preclusive courses
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KIV/SU and KKY/USK
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Prerequisite courses
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N/A
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Informally recommended courses
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KMA/LAA and KMA/MA1 and KMA/PSA and KIV/TI
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Courses depending on this Course
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KIV/NLP, KIV/SZD
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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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To provide the students with the necessary theoretical knowledge and practical skills to understand the fundamental principles of machine learning techniques as of the key area of artificial intelligence; to understand in depth both theoretically and practically the basic methods from which the current modern machine learning techniques and techniques of knowledge representation and transformation are derived. Emphasis is placed on the interconnection of the related knowledge from mathematics, theoretical computer science, probability and statistics, and other theoretical prerequisites with a practical engineering approach to machine learning techniques and practices for the implementation and deployment of artificial intelligence in industry.
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Requirements on student
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A student is entitled to a credit if he/she successfully (with a rate of 51% and more) passes the credit test and obtains at least half of the possible points from small, continuously assigned practice tasks. The purpose of the credit test is to verify whether the student is sufficiently familiar with the basic prerequisite skills and knowledge, so that students who do not have sufficient knowledge to pass the exam checked in.
The examination has a form of an individual oral exam which verifies the depth of understanding of the problems that the subject deals with. The student draws 1 question out of the list of exam questions, then he/she has 30 minutes to prepare him/herself (in written form, preferably) and then answers the question in details. In the case of a large number of enrolled students, the examiner may, instead of an oral examination, impose a written test with an extent roughly equivalent to the oral examination.
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Content
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The below itemized topics represent radii of the subject matter went through, they do not exactly correspond to scheduled lectures:
1. Introductory information, organization of the subject, recommended literature and sources of study materials; basic notion and definitions and their mutual relations; relationship among data, information and knowledge; definition of machine intelligence and machine learning (hypothesis - knowledge, state space, parametric space).
2. Introduction into machine learning, supervised and unsupervised learning; applications and examples, case studies; description of the purpose and procedure of each part of a learning system; machine learning operators; canonic machine learning task, its preconditions and goals; regression and classification, primitive linear classifier.
3. Bayes learning, Bayes theorem, optimal and naive bayesian classifier, hypothesis selection strategies, applications of NBC.
4. Linear regression, cost function derivation and techniques to minimize it, gradient descent derivation, gradient descent algorithm.
5. Multivariate linear regression, gradient descent in multidimensional space, problems and limitations of gradient descent; polynomial regression; normal equation.
6. Logistic regression, logistic regression hypothesis model, interpretation of the results, decision boundary, multi-class classification - One-vs-All algorithm.
7.Regularization, overtraining and its symptoms, techniques to avoid/suppress overtraining, naive derivation of regularization, regularization algorithm, regularized linear and logistic regression.
8. Support Vector Machines, optimization goal as an alternative perspective of logistic regression, mathematical model of SVM, hypothesis with safety factor, kernels.
9. Neural networks, history, biological pre-model of artificial neural networks, mathematical model of a neuron, MLP-type layered networks, classification via ANN, cost function of an ANN and its optimization, learning, Backpropagation algorithm.
10. Clustering, general remarks on unsupervised learning, K-means method, optimization criterion of the K-means, centroid selection, cluster number selection, K-means algorithm.
11. Dimensionality reduction, Principal Component Analysis, PCA functionality description and algorithm, PCA features, mathematical background of PCA, applications and case studies.
12. Blind source separation, motivation and definition of the blind source separation problem, Independent Component Analysis, ICA functionality description and algorithm, ICA features, mathematical background of ICA, applications and case studies.
13. Evolutional and genetic algorithms, metaheuristic strategies of state space search; genotype encoding techniques; operators a parameters of GA, fitness function; general canonic form of a genetic algorithm, SOEA/MOEA; new generation selection strategies.
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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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Basic:
Bishop, C.M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0387-31073-2.
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Basic:
Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. Springer. 2009. ISBN 978-0-387-84857-0.
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Recommended:
Barber, David. Bayesian reasoning and machine learning. Cambridge : Cambridge University Press, 2012. ISBN 978-0-521-51814-7.
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Recommended:
Smola, A. J., Vishwanathan, S. V. N. Introduction to Machine Learning. Cambridge: Cambridge University Press, 2008. ISBN 0-521-82583-0.
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Recommended:
Nilsson, J. Nils. Introduction to Machine Learning. Stanford University Press. Stanford University, 2005.
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Recommended:
Murphy, Kevin P. Machine learning : a probabilistic perspective. Cambridge : MIT Press, 2012. ISBN 978-0-262-01802-9.
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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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Graduate study programme term essay (40-50)
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40
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Preparation for an examination (30-60)
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30
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Preparation for laboratory testing; outcome analysis (1-8)
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15
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Practical training (number of hours)
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26
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Contact hours
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39
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Presentation preparation (report) (1-10)
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6
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Total
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156
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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: |
Good command of mathematic analysis, calculus, probability & statistics, and numerical methods. Active programming skills in a high-level language like e.g. C/C++, Object Pascal, Java, C#; MATLAB or Octave command is welcome. The ability of self-reliant study of scientific literature and a satisfactory English language level (presumed study from mostly English resources). |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
Write non-trivial programs in a high-level language like e.g. C/C++, Object Pascal, Java, C#; MATLAB or Octave command is welcome. Study the scientific literature written in English. |
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 |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
Through passing the subject, a student gains a general overview of the paradigms of artificial cognitive systems focused mainly on their practical application in the field of artificial intelligence and intelligent software. He/she accomplishes a deep understanding of the basic techniques of machine learning, representation, derivation, and recording of the knowledge and rational behaviour, i.e. decision making and problem solving. This allows him/her to become involved in research and development tasks both in his/her subsequent study and in industrial practice. |
Skills - skills resulting from the course: |
A student can implement basic machine learning techniques or modify them with deep understanding. He/she can also design own well-grounded approaches for solving problems in the field of artificial intelligence and machine learning. |
Competences - competences resulting from the course: |
N/A |
N/A |
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: |
Test |
Skills demonstration during practicum |
Oral exam |
Skills - skills achieved by taking this course are verified by the following means: |
Oral exam |
Test |
Skills demonstration during practicum |
Competences - competence achieved by taking this course are verified by the following means: |
Oral exam |
Test |
Skills demonstration during practicum |
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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 |
Lecture supplemented with a discussion |
Practicum |
Task-based study method |
Self-study of literature |
Individual study |
Interactive lecture |
Discussion |
Skills - the following training methods are used to achieve the required skills: |
Practicum |
Individual study |
Competences - the following training methods are used to achieve the required competences: |
Lecture supplemented with a discussion |
Discussion |
Task-based study method |
Self-study of literature |
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