Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: Up to 36 hours of lectures |
Total Time Commitment:
Enrolment in a research higher degree (Masters or PhD) in Engineering
|Recommended Background Knowledge:|| |
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|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/
CoordinatorProf Girish Nair
Dr. Marcus Nathan Brazil
Information Theory provides the fundamental backbone of reliable communications, reliable data storage, and data compression. This subject provides the rigorous basis of `information', showing it to have deep links to randomness, the ability to reduce data to its essence, and to the ultimate limits to communication.
This subject is aimed at postgraduate (research) students. The subject material covers the core topics of Information Theory including: Shannon entropy, Mutual Information, lossless and lossy source coding, Shannon's celebrated channel capacity and channel coding theorem, differential entropy and the Gaussian channel. In addition other topics are selected from rate distortion theory, network information theory, distributed source coding, Kolmogorov Complexity, and possibly other related applications in communications theory and statistical inference. Technically the subject combines probabilistic models of idealised communication and the mathematics of applied probability.
INTENDED LEARNING OUTCOMES (ILO)
Having completed this subject it is expected that the student be able to:
Hurdle requirement: Students must pass the written exam to pass the subject.
Intended Learning Outcomes (ILOs) 1-5 are assessed in the final exam and submitted assignments.
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This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
LEARNING AND TEACHING METHODS
The subject is delivered through lectures and homework assignments
INDICATIVE KEY LEARNING RESOURCES
Students are provided with lecture notes, including worked examples, assignment problems, and recommended reading lists comprising textbooks and journal articles.
CAREERS / INDUSTRY LINKS
Exposure to research literature and the rigour expected at the level of postgraduate study.
Master of Philosophy - Engineering |
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