Postgraduate Course: Information Theory (INFR11087)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Year 4 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	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.
Course description	-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

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser. - A solid mathematical background is required. - Essential maths knowledge: Special functions log, exp are fundamental; mathematical notation (such as sums) use throughout; some calculus. - Probability theory will be used extensively: Random variables, expectation, Bernoulli trials, Binomial distribution, joint and conditional probabilities. - A basic level of programming is assumed and not covered in lectures. The assessed assignment will involve programming in a language or your choice.

Information for Visiting Students
Pre-requisites	Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course lecturer.

Course Delivery Information

Academic year 2014/15, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 70 )
Assessment (Further Info)	Written Exam 80 %, Coursework 20 %, Practical Exam 0 %
Additional Information (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. You should expect to spend approximately 20 hours on the coursework for this course. If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			2:00

Academic year 2014/15, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 70 )
Assessment (Further Info)	Written Exam 80 %, Coursework 20 %, Practical Exam 0 %
Additional Information (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. You should expect to spend approximately 20 hours on the coursework for this course. If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)			2:00

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.

Reading List
ESSENTIAL: "Information Theory, Inference and Learning Algorithms", David MacKay, CUP, 2003. http://www.inference.phy.cam.ac.uk/mackay/itila/book.html BACKGROUND ONLY: Elements of Information Theory, 2nd Edition, Cover and Thomas, Wiley 2006

Additional Information
Course URL	http://course.inf.ed.ac.uk/it
Graduate Attributes and Skills	Not entered
Keywords	Not entered

Contacts
Course organiser	Dr Iain Murray Tel: (0131 6)51 9078 Email: I.Murray@ed.ac.uk	Course secretary	Miss Claire Edminson Tel: (0131 6)51 4164 Email: C.Edminson@ed.ac.uk