Бакалавриат
2024/2025
Компьютерное зрение
Статус:
Курс по выбору (Прикладной анализ данных)
Направление:
01.03.02. Прикладная математика и информатика
Где читается:
Факультет компьютерных наук
Когда читается:
4-й курс, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Копылов Иван Станиславович
Язык:
английский
Кредиты:
5
Course Syllabus
Abstract
This course provides a comprehensive, engineering-oriented introduction to modern computer vision. Students will learn the complete pipeline from classical image processing to state-of-the-art transformer-based architectures. The course covers CNNs, Vision Transformers, object detection (YOLO, DETR), segmentation (U-Net, Mask R-CNN, SAM), multimodal models (CLIP), self-supervised learning, generative models (VAE, GAN), and video understanding. Practical sessions include hands-on implementation, training, and deployment of CV models.
Learning Objectives
- The main purpose is to provide students with both theoretical understanding and practical skills in modern computer vision. Students will master the evolution from classical filters to deep learning architectures, understand the mathematical foundations of CNNs and Transformers, and gain engineering competence in deploying CV systems.
Expected Learning Outcomes
- Understand various topics in computer vision, including image processing, feature extraction, object recognition, tracking, and autonomous driving.
- Gain practical experience through assignments such as panorama stitching, face recognition, and descriptors for image retrieval.
- Develop critical thinking and problem-solving skills through the coursework and discussions.
- Be prepared for future internships, research projects, and job opportunities in the field of computer vision.
- Understand transformers and multimodal computer vision concepts.
- Apply the knowledge and skills gained in the course to real-world computer vision applications.
Course Contents
- Object detectors
- Image segmentation
- Image conversion and generation
- Video Processing Basics
- Image Processing
- Feature Extraction
- Object Recognition
- Tracking
- Autonomous Driving
- Transformers
- Multimodal Computer Vision
- Assignments
- Problem-solving and Critical Thinking
- Real-world Applications
