Visual recognition
General data
Course ID: | 1000-318bVR |
Erasmus code / ISCED: |
11.3
|
Course title: | Visual recognition |
Name in Polish: | Visual recognition |
Organizational unit: | Faculty of Mathematics, Informatics, and Mechanics |
Course groups: |
(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science Obligatory courses for 1st year Machine Learning Specific programme courses of 2nd stage Bioinformatics |
ECTS credit allocation (and other scores): |
5.00
|
Language: | English |
Type of course: | elective monographs |
Requirements: | Deep neural networks 1000-2M16GSN |
Short description: |
The goal of the course is to present deep learning architectures as well as to teach implementation, training and debugging own neural networks dedicated to visual recognition. Students gain theoretical knowledge, information on the state of the current research in the domain and obtain practical skills in visual recognition. |
Full description: |
1. Introduction to Visual Recognition (classic methods: SIFT, Hough transform). 2. Convolutional Neural Networks - recap. 3. Visualising and Understanding. 4. Object Detection. 5. Semantic and Instance Segmentation. 6. Video understanding. 7. 3D vision. 8. Generative models. |
Bibliography: |
http://www.deeplearningbook.org/ |
Learning outcomes: |
Knowledge: the student * has based in theory and well organized knowledge of problems of image classification and object detection [K_W12]. Abilities: the student is able to * create a developed solution in the domain of image classification and object detection [K_U15]. Social competences: the student is ready to * critically evaluate acquired knowledge and information [K_K01]; * recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution [K_K02]; * think and act in an entrepreneurial way [K_K03]. |
Assessment methods and assessment criteria: |
Laboratories: programming projects. Lectures: written examination |
Classes in period "Summer semester 2023/24" (in progress)
Time span: | 2024-02-19 - 2024-06-16 |
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MO TU WYK
LAB
LAB
W TH FR LAB
|
Type of class: |
Lab, 30 hours
Lecture, 30 hours
|
|
Coordinators: | Piotr Biliński | |
Group instructors: | Piotr Biliński, Spyridon Mouselinos, Marcin Możejko, Konrad Staniszewski | |
Students list: | (inaccessible to you) | |
Examination: | Examination |
Classes in period "Summer semester 2024/25" (future)
Time span: | 2025-02-17 - 2025-06-08 |
Navigate to timetable
MO TU W TH FR |
Type of class: |
Lab, 30 hours
Lecture, 30 hours
|
|
Coordinators: | Piotr Biliński | |
Group instructors: | Piotr Biliński, Jacek Cyranka, Spyridon Mouselinos, Alicja Ziarko | |
Students list: | (inaccessible to you) | |
Examination: | Examination |
Copyright by University of Warsaw.