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About this Course
Page history
last edited
by sidjaggi 11 years, 1 month ago
IEG5154 Information Theory
Semester: Spring 2009
Course Title: IEG 5154 Information Theory

Description: The course aims to cover
 Fundamental definitions of information measure (entropy, conditional entropy, mutual information) and their properties.
 Lossless Source Coding/Data Compression  theory and algorithms
 Channel Coding/ErrorCorrecting Codes/Coding theory
 Ratedistortion theory.

Miscellaneous advanced topics depending on time and interest (Kolmogorov Complexity, Universal Portfolio Theory, Network Coding, ...)

Content, highlighting fundamental concepts (not necessarily in the chronological order we will use in classroom discussions)
Topic

Contents/fundamental concepts

 Information theoretic quantities

 Entropy, conditional entropy, mutual information, divergence, differential entropy, Markov/ergodic sources, properties (chain rules, positivity, convexity, Jensen's inequality, Fano's inequality, conditioning, data processing inequality, Law of large numbers, Sanov's theorem, AEP, entropy rates)
 Achievability and converse proofs, Kraft inequality, codes  ShannonFanoElias, Huffman, Arithmetic, LempelZiv (universal codes)
 Hamming codes, channel capacity (achievability and converse proofs), zeroerror capacity, joint sourcechannel coding, feedback capacity, Gaussian channels (parallel channels, coloured noise)
 Scalar/vector quantization, ratedistortion theorem (achievability and converse)

Learning outcomes:
 Demonstrate ability to manipulate basic informationtheoretic quantities to prove relevant theorems.
 Demonstrate understanding of foundational topics in information theory, and an ability to use the theoretic tools required to prove corresponding theorems.
 Use the above to characterize and design information storage, manipulation and transmission systems.

Learning activities
Lecture

Problem Sets

Online Activities (Scribe Notes/Discussion)

Homeworks

(hr) in class

(hr) in/out class

(hr) out of class

(hr) out of class

36

0

36

12

12

6

15

0

M

O

M

O

M

O

M

O

M: Mandatory activity in the course
O: Optional activity
NA: Not applicable
Assessment scheme
Task nature

Description

Weight

Problem sets
Homeworks
Scribe Notes
Class participation
Final Exam

Inclass problems, handed in next class
Collaborative homeworks
Scribe notes of a particular lecture
Inclass discussion/Discussion on wiki
Examination

20% (8)
20% (4)
20% (2)
15%
25%

Learning resources for students
Feedback for evaluation:
Students are welcome to express their comments and suggestions via the following formal and informal feedback channels:
 Two course evaluations. First one to be conducted in the middle of the term and the second one at the end of the term. Students are encouraged to provide specific comments and/or suggestions in addition to the numeric ratings.
 At the end of each lecture there will be a single question feedback slip given to each student.
 Students are also encouraged to provide feedbacks using informal channels, such as email/discussion to instructor/tutor, and via the talk pages on the class wiki.
Tentative Course Schedule (will edit as we go along)
DATE 
TOPIC 
READINGS 
5 Jan 
Logistics/Introduction. Worstcase compression. Binary trees. 

9 Jan 
Method of types, typical/atypical sets, sizes/probabilities, Sanov's Theorem 
11.111.5, Cover/Thomas 
12 Jan 
Sanov's Theorem  Achievability/Converse of Source Coding Theorem

11.111.5, 2.12.6, 2.8, 2.10, Cover/Thomas 
16 Jan 
Entropy definitions in terms of typical sets, properties. 
11.111.5, 2.12.6, 2.8, 2.10, Cover/Thomas 
19 Jan 
Properties of entropytype functions. 
2.12.6, 2.8, 2.10, Cover/Thomas 
23 Jan 
Informationtheoretic proof of Source Coding Theorem 
7.17.7, 7.9, Cover/Thomas 
2 Feb 
Discussion of Channel Coding Theorem 
7.17.7, 7.9 Cover/Thomas 
6 Feb 
Proof of Channel Coding Theorem 
7.67.7,7.9 Cover/Thomas 
9 Feb 
Classifications and Properties of PerSymbol Source Coding Schemes 
5.15.5 Cover/Thomas 
13 Feb 
Shannon Code, Huffman Code 
5.6 Cover/Thomas 
20 Feb 
Entropy Rates of a Stochastic Process 
4 Cover/Thomas 
23 Feb 
Arithmetic Code, Weak Typicality 
5.9, 13.3, 3 Cover/Thomas 



















































Teachers’ or TA’s contact details
Professor/Lecturer/Instructor:


Name:

Prof. Sidharth Jaggi

Office Location:

SHB Room 706

Telephone:

26094326

Email:

jaggi [AT] ie {dot} cuhk {dot} edu {dot} hk

Teaching Time and Venue:

Mon 11:30am to 1:00pm, (ELB 202),
Fri 3:30pm to 5:00pm, (ERB 703).

Website:

http://ieg5154.pbwiki.com

Other information:

Office Hours: by appointment

Teaching Assistant/Tutor:


Name:

Mr. Cho Yiu Ng (Michael)

Office Location:

SHB Room 826A

Telephone:

26098383

Email:

michaelng [AT] ieee {dot} org

Website:

http://ieg5154.pbwiki.com

Other information:

Office Hours: Thu 5:30pm6:15pm

A facility for posting course announcements
Academic honesty and plagiarism
Attention is drawn to University policy and regulations on honesty in academic work, and to the disciplinary guidelines and procedures applicable to breaches of such policy and regulations. Details may be found at http://www.cuhk.edu.hk/policy/academichonesty/ . With each assignment, students will be required to submit a statement that they are aware of these policies, regulations, guidelines and procedures.

Additional Resources:
About this Course

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Comments (3)
MEI Yuchen said
at 4:43 pm on Jan 6, 2009
Do we have tutorial class?
Cho Yiu Ng said
at 7:36 pm on Jan 6, 2009
No. But I reserve Thursday 5:30pm6:15pm to be the office hour for this course. I'll be in my office to offer any helps for this course.
sidjaggi said
at 7:46 pm on Jan 6, 2009
Also, you're welcome to make appointments via email to see me or Michael any time you have problems that can't be dealt with in/immediately after class. My calendar is at http://calendar.jaggi.name
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