Postgraduate Course: Information Theory (INFR11087)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/it/
|
Taught in Gaelic? |
No |
| Course description |
Information theory describes the fundamental limits on our ability to store, process and communicate data, whether in natural or artificial systems. Understanding and approaching these limits is important in a wide variety of topics in informatics.
This course covers the theory introduced by Shannon in 1948, which revolutionized how we think about information and communication, and some of the practical techniques for compression and reliable communication that have been developed since. |
Entry Requirements
| Pre-requisites |
|
Co-requisites |
|
| Prohibited Combinations |
|
Other requirements |
- A solid mathematical background is required.
- Some general mathematical ability (at MfI 1 level) is essential: Special functions log, exp are fundamental; mathematical notation (such as sums) used throughout; some calculus.
- Probability theory is used extensively: Random variables, expectation, Bernoulli trials, Binomial distribution, joint and conditional probabilities (MfI 1&4).
|
| Additional Costs |
None |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
No |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | 14:00 - 14:50 | | | | | Central | Lecture | | 1-11 | | | | | 14:00 - 14:50 |
| First Class |
Week 1, Tuesday, 14:00 - 14:50, Zone: Central. Room G.04, William Robertson Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S2 (April/May) | | 2:00 | 12 sides | |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | 14:00 - 14:50 | | | | | Central | Lecture | | 1-11 | | | | | 14:00 - 14:50 |
| First Class |
Week 1, Tuesday, 14:00 - 14:50, Zone: Central. Room G.04, William Robertson Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S1 (December) | | 2:00 | 12 sides | |
Summary of Intended Learning Outcomes
By taking this course, students should be able to:
- Explain the source coding and noisy channel theorems and describe their implications for applications covered in lectures.
- Compute information theoretic quantities, construct bounds and describe+implement algorithms involving high-dimensional probability distributions.
- Describe the techniques covered in the course: identify their limitations, discuss their practical merits and design and describe alternatives.
- For a novel data source, communication channel or application, identify relevant information theoretic aspects to provide insight or suggest useful methods. |
Assessment Information
Written Examination: 80%
Assessed Assignments: 20%
Oral Presentations: 0%
Assessment:
The assessed assignments will test that students are keeping up with the material and test an ability to experiment and implement codes while solving problems. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
-Differential Entropy and information content
-Source coding theorem
-Symbol codes, Kraft-McMillan inequality, Huffman codes
-Stream codes, adaptive models, arithmetic coding
-Compression in practice
-Relative Entropy, mutual information, related inequalities
-Noisy channel coding theorem, channel capacity
-Error correcting codes
-Codes robust to erasures
-Lossy compression
-Hash codes |
| Transferable skills |
Not entered |
| Reading list |
"Information Theory, Inference and Learning Algorithms", David MacKay, CUP, 2003. http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
|
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 6
Timetabled Laboratories 0
Non-timetabled Assessed Assignments 20
Private Study/Other 54
Total 100 |
| Keywords |
Not entered |
Contacts
| Course organiser |
Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary |
Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 7:53 am
|
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