Weizmann Institute of Science 20234031 Deep Learning for Computer Vision: Fundamentals and Applications
Winter 2022/3
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Course Overview

This course covers the fundamentals of deep-learning based methodologies in area of computer vision. Topics include: core deep learning algorithms (e.g., convolutional neural networks, transformers, optimization, back-propagation), and recent advances in deep learning for various visual tasks. The course provides hands-on experience with deep learning for computer vision: implementing deep neural networks and their components from scratch, tackling real world tasks in computer vision by desigining, training, and debugging deep neural networks using leading mainly PyTorch.

Announcements

• We encourage students to take "Basic Topics I" (20234041) in conjuction with this course.
• Only first three lectures of the course will be taught via zoom. The rest of the course is frontal.
• All course materials will be made available in the moodle.
• Make sure you have completed all the prerequisites.

Course Information

Course Instructors

Assaf Shocher

Shai Bagon

Meirav Galun

Tali Dekel

Teaching Assistants

Dror Moran

Rafail Fridman

Niv Haim

Dana Joffe

Yaniv Nikankin

Please use the course Moodle page for all communication with the teaching staff.

Lectures and tutorials time

Lectures: 9:15-11:00 on Mondays

Tutorials: 9:15-11:00 on Wednesdays

Office Hours

Upon request

Grading Policy

Final grade will be given based on assignments and a final project.
There will be no exam in the course.
Note that different assignments might have different cntribution to the overall grade.

Previous semesters

Winter 2021/2.

Spring 2021.

 

 

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The template of this website is based on CSAIL MIT's Advanced Computer Vision course