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About this Course
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Saved by Cho Yiu Ng
on February 23, 2009 at 4:23:31 pm
IEG5154 Information Theory
Semester: Spring 2009
Course Title: IEG 5154 Information Theory
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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/Error-Correcting Codes/Coding theory
- Rate-distortion theory.
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Miscellaneous advanced topics depending on time and interest (Kolmogorov Complexity, Universal Portfolio Theory, Network Coding, ...)
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Content, highlighting fundamental concepts (not necessarily in the chronological order we will use in classroom discussions)
Topic
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Contents/fundamental concepts
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- Information theoretic quantities
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- 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 -- Shannon-Fano-Elias, Huffman, Arithmetic, Lempel-Ziv (universal codes)
- Hamming codes, channel capacity (achievability and converse proofs), zero-error capacity, joint source-channel coding, feedback capacity, Gaussian channels (parallel channels, coloured noise)
- Scalar/vector quantization, rate-distortion theorem (achievability and converse)
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Learning outcomes:
- Demonstrate ability to manipulate basic information-theoretic 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.
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Learning activities
Lecture
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Problem Sets
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Online Activities (Scribe Notes/Discussion)
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Homeworks
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(hr)
in class
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(hr)
in/out class
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(hr)
out of class
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(hr)
out of class
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36
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0
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36
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12
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12
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6
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15
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0
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M
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O
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M
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O
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M
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O
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M
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O
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M: Mandatory activity in the course
O: Optional activity
NA: Not applicable
Assessment scheme
Task nature
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Description
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Weight
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Problem sets
Homeworks
Scribe Notes
Class participation
Final Exam
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In-class problems, handed in next class
Collaborative homeworks
Scribe notes of a particular lecture
In-class discussion/Discussion on wiki
Examination
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20% (8)
20% (4)
20% (2)
15%
25%
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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. Additionally, 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. Worst-case compression. Binary trees. |
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9 Jan |
Method of types, typical/atypical sets, sizes/probabilities, Sanov's Theorem |
11.1-11.5, Cover/Thomas |
12 Jan |
Sanov's Theorem -- Achievability/Converse of Source Coding Theorem
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11.1-11.5, 2.1-2.6, 2.8, 2.10, Cover/Thomas |
16 Jan |
Entropy definitions in terms of typical sets, properties. |
11.1-11.5, 2.1-2.6, 2.8, 2.10, Cover/Thomas |
19 Jan |
Properties of entropy-type functions. |
2.1-2.6, 2.8, 2.10, Cover/Thomas |
23 Jan |
Information-theoretic proof of Source Coding Theorem |
7.1-7.7, 7.9, Cover/Thomas |
2 Feb |
Discussion of Channel Coding Theorem |
7.1-7.7, 7.9 Cover/Thomas |
6 Feb |
Proof of Channel Coding Theorem |
7.6-7.7,7.9 Cover/Thomas |
9 Feb |
Classifications and Properties of Per-Symbol Source Coding Schemes |
5.1-5.5 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 |
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Teachers’ or TA’s contact details
Professor/Lecturer/Instructor:
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Name:
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Prof. Sidharth Jaggi
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Office Location:
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SHB Room 706
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Telephone:
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2609-4326
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Email:
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jaggi [AT] ie {dot} cuhk {dot} edu {dot} hk
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Teaching Time and Venue:
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Mon 11:30am to 1:00pm, (ELB 202),
Fri 3:30pm to 5:00pm, (ERB 703).
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Website:
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http://ieg5154.pbwiki.com
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Other information:
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Office Hours: by appointment
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Teaching Assistant/Tutor:
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Name:
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Mr. Cho Yiu Ng (Michael)
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Office Location:
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SHB Room 826A
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Telephone:
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2609-8383
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Email:
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michaelng [AT] ieee {dot} org
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Website:
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http://ieg5154.pbwiki.com
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Other information:
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Office Hours: Thu 5:30pm-6:15pm
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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.
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Additional Resources:
About this Course
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