Weizmann Institute of Science


Deep Learning for Computer Vision:
Fundamentals and Applications

Spring 2021

[Home | Schedule | Final Project | Piazza]

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, 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 frameworks (PyTorch, Tensorboard).


Course Information

Course Instructors

Teaching Assistants

Please use the course Piazza page for all communication with the teaching staff

Niv Granot
Dror Moran
Akhiad Bercovich
Ben Feinstein
Shir Amir

Lectures and tutorials time

Lectures: 9:15-11:15 on Mondays

Tutorials: 11:15-12:15 on Thursdays

Office Hours

Upon request

Grading Policy

Assignments: 60%, Final project: 40%



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