Advanced Topics in Computer Vision and Deep Learning
Lecturers and Teaching Assistants
Prof. Michal Irani, Dr. Shai Bagon, Prof. Shimon Ullman, Dr. Tali Dekel
Course Schedule and Location
Tuesday, 10:15 - 12:00, Ziskind, Rm 1
Field of Study, Course Type and Credit Points
Mathematics and Computer Science: Seminar; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Advanced Reading-Group Seminar.
Absence from lessons must be for a justified reason, and with prior approval.
Priority will be given to students from the Math and CS faculty.
Both these courses are mandatory prerequisites:
- Introduction to computer vision.
- Deep Learning for Computer Vision: Fundamentals and Applications.
Unless the student took an equivalent course in another university, in which case they will have to point to the specific course they took.
Attendance and participation
Grade Breakdown (in %)
The grade is based on a presentation, attendance, and reading papers
Estimated Weekly Independent Workload (in hours)
This course will cover important advances and recently published papers in Computer Vision and Deep Learning.
Upon successful completion of this course students should be able to:
Become familiar with advances and recently published papers in the area of Deep Learning and applications to Computer Vision. In addition, an emphasis will be put on how to prepare a good presentation.
List of papers to read will be given at the beginning of the course.