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HU Credits:
4
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Computer Sciences
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Or Ordentlich
Coordinator Office Hours:
TBA
Teaching Staff:
Prof Or Ordentlich,
Mr. Tomer Berg
Course/Module description:
The course introduces the basic information measures: entropy, mutual information and divergence, and illustrates how to use those quantities for developing and analyzing performance of compression, communication and statistical inference systems.
Course/Module aims:
To form a gentle introduction to the fascinating field of information theory
Learning outcomes - On successful completion of this module, students should be able to:
-Explain the basic ideas underlying lossless compression and design low-complexity lossless compression systems.
-Explain the basic ideas underlying reliable communication over a noisy channel.
-Compute the fundmemtal limits for basic problems in statistical inference, communication and compression.
Attendance requirements(%):
Teaching arrangement and method of instruction:
Lecture + recitation
Course/Module Content:
-Information measures
-Losslsess compression for sources with a known/unknown statistical model
-Fano’s inequality and its application
-Applications of information theory in statistics
-Communication over a noisy channel and the channel capacity theorem
Required Reading:
Lecture note that will be uploaded to the course's website
Additional Reading Material:
Cover, T. M., and Joy A. Thomas. "Elements of information theory." (2006).
Course/Module evaluation:
End of year written/oral examination 90 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 10 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
The course is intended for undergraduate students. In the academic year 2022-2023 only, it will also be open for graduate students (since "introduction to information and inference will not be taught that year).
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