|
HU Credits:
4
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Computer Sciences
Semester:
2nd Semester
Teaching Languages:
English and Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Raananm Fattal
Coordinator Office Hours:
After class
Teaching Staff:
Prof. Raanan Fattal,
Mr. shahar edelman
Course/Module description:
The course will describe various common network architectures and tools for applying them in different fields of CS
Course/Module aims:
Getting to know deep-learning tools and implementing them.
Learning outcomes - On successful completion of this module, students should be able to:
Understand these tools and being able to apply them in practice.
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Lecture
Course/Module Content:
Different layers of current neural networks, RNNs, CNNs, GANs, AEs, Diffusion, Attention, classifiers, optimisation, and some theoretical insights as to the functional spaces they span.
Required Reading:
Will be given in the Moodle
Additional Reading Material:
Grading Scheme :
Written / Oral / Practical Exam / Home Exam 60 %
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 40 %
Additional information:
Prerequisite courses:
Intro. to Prob. and Stat. (80430), or Into. to Machine Learning (67577)
|
