Course Detail

ELE 637 Fundamentals of Information Theory
2017-2018 Fall term information

The course is open this term
Supervisor(s):Dr. Emre Aktaş
SSMonday09:00 - 11:45

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.


Course Name Code Semester Theory
Credit ECTS
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
Instructor (s)Dr. Emre Aktaş 
Course objectiveThe objective of the course is to introduce ? the notion of entropy and information ? the fundamental limits of data compression ? the fundamental limits of data transmission systems.  
Learning outcomes
  1. Learn and use the main mathematical tools of information theory that quantify and relate information
  2. Learn fundamental limits for systems that store and compress data
  3. Learn fundamental methods of source coding
  4. Learn fundamental limits for systems that communicate data
  5. Utilize information theory in order to gain insight of and design any system that stores, processes, or communicates information
Course ContentIntroduction, review of probability,
Entropy, relative entropy, mutual information, inequalities,
The asymptotic equipartition property,
Data compression,
Channel capacity,
Differential entropy, the Gaussian channel,
Network information theory.
ReferencesElements of Information Theory, Cover and Thomas, Wiley Interscience
Gallager, "Claude E. Shannon: A Retrospective on His Life, Work, and Impact", IEEE
Trans. Inform. Theory, vol.47, no.7, Nov. 2001
Wyner, "Fundamental Limits in Information Theory", Proc. of the IEEE, vol.69, no.2,
Feb. 1981
Verdu, "Fifty Years of Shannon Theory", IEEE Trans. Inform. Theory, vol.44, no.6,
Oct. 1998

Course outline weekly

Week 1Review of probability theory, entropy
Week 2Relative entropy and mutual information
Week 3Jensen?s inequality and its consequences
Week 4Asymptotic equipartition property
Week 5Data compression and Kraft inequality
Week 6Optimal codes, Huffman codes
Week 7Shannon-Fano-Elias coding
Week 8Midterm Exam
Week 9Channel capacity examples
Week 10Channel coding theorem
Week 11Fano?s inequality and the converse to the coding theorem
Week 12Differential entropy
Week 13Gaussian channel
Week 14Network information theory
Week 15Final exam
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Field activities00
Specific practical training00
Final exam150
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)148112
Presentation / Seminar Preparation000
Homework assignment155
Midterms (Study duration)000
Final Exam (Study duration) 12525
Total Workload3166209

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.  X  
4. Designs and runs research projects, analyzes and interprets the results.   X 
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.  X  
6. Produces novel solutions for problems.   X 
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.    X
8. Follows technological developments, improves him/herself , easily adapts to new conditions.  X   
9. Is aware of ethical, social and environmental impacts of his/her work.X    
10. Can present his/her ideas and works in written and oral form effectively; uses English effectivelyX    

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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