/ Convolutional Neural Networks for Visual Recognition
Course: Artificial Intelligence
Structural unit: Faculty of Computer Science and Cybernetics
Title
/ Convolutional Neural Networks for Visual Recognition
Code
ДВС.2.05
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
4
Learning outcomes
Form of study
Prerequisites and co-requisites
1. To know: the basic disciplines - "Mathematical Analysis", "Linear Algebra and Geometry", "Probability Theory and Mathematical Statistics", "Programming", "Computational Geometry and Computer Graphics", "Algorithms and Data Structures", "Machine Learning" .
2. To be able to: develop, analyze and apply algorithms and software to solve problems and applied tasks using modern software development methods.
Course content
Discipline aim. The aim of the discipline is to acquaint students with the basics of convolutional neural networks as one of the main branches of artificial intelligence, and modern methods for solving problems of computer vision and image processing, including obtaining semantic and metric information from images. To prepare the student for the effective use of modern methods such as machine learning methods to create artificial intelligence systems in further professional activities; help to acquire skills of practical work with modern software for building intelligent models.
Recommended or required reading and other learning resources/tools
4. Lynda Shapyro, Dzhordzh Stokman. Kompiuternoe zrenye. Laboratoryia znanyi. 2013
5. Stephen Marsland. Machine Learning: An Algorithmic Perspective, 452 р., 2015.
6. Christopher M Bishop. Pattern recognition. Machine Learning, 128 p., 2006.
7. Ethem Alpaydin. Introduction To Machine Learning, 584 p., 2009.
8. Tom M. Mitchell. Machine Learning [http://www.cs.cmu.edu/~tom/mlbook.html]
9. Yaser S. Abu-Mostafa. Learning from data, 215 p., 2017
10. Alex Smola. Introduction to Machine Learning, 234 p., 2008.
11. Liuher Dzh.F. Yskusstvennыi yntellekt: stratehyy y metodы reshenyia slozhnыkh problem. – M.: Vyliams, 2005. – 864 s.
12. Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction, 764 p., 2008.
Planned learning activities and teaching methods
Lecture,Laboratory work, individual work
Assessment methods and criteria
- semester examination:
1. Active work during a lection: PH1.1, PH1.2, PH1.3, PH1.4;
2. Execution of individual tasks: PH2.1, PH2.2, PH2.3;
3. Test 1: PH1.1, PH1.2;
4. Test 2: PH1.3;
5. Test 3: PH1.4;
- final examination: final test.
- maximum: 40 points;
- studying results for examination: PH1.1, PH1.2, PH1.3, PH1.4;
- task form: writing.
Language of instruction
Ukrainian language
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline