# Difference between revisions of "NME130/Information theory"

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#** Can probably be done in 1-2 lectures of 1.5 hours each | #** Can probably be done in 1-2 lectures of 1.5 hours each | ||

* Entropy will have be introduced, but probably not entropy rate | * Entropy will have be introduced, but probably not entropy rate | ||

+ | * Should be enough to touch on Bode/Shannon pictures | ||

+ | * Should also be able to talk about stochastic versus worst case | ||

+ | |||

+ | === Tracey == | ||

+ | |||

+ | ==== Error correction coding ==== | ||

+ | # Coverage | ||

+ | * High level concetnrs, framework, assumptions | ||

+ | * Connections with other fields | ||

+ | * Details of a few illustrative results | ||

+ | # Avoid excessive dpulication of material covered in EE 127, 127 | ||

+ | * Want to impart a basic knowledge of what are some connections between them and other fields, so that students will have a basis for deciding if they want to go deeper | ||

+ | |||

+ | ==== Topics === | ||

+ | # Framework and assumptions (1 hr) | ||

+ | #* Differences between information theory and coding theory | ||

+ | #* Differences between stoachastic and adversarial noise | ||

+ | #* Block length, complexity, etc (coding theory works with constraints, etc) | ||

+ | # Upper bounds on codes (2 hr) | ||

+ | # Classes of codes: random codes, algebraic coes, sparse graph codes (2-3 hr) | ||

+ | # Decoding techniques (algebraic, sum product algorithm aand special cases, LP decoding) (2-3hr) | ||

+ | # Networking coding and its relation to network information theory, coding thoery and networking optimization (2-3 hr) | ||

+ | # Connections with other fields (learning, cryptography) (2-3 hr) | ||

=== Tracy === | === Tracy === |

## Revision as of 19:33, 27 May 2009

### Michelle

- Tried to figure out what people wanted to see
- Decided that the way to go is to pull out a small piece that can be done in its entirety, but gives a sense of the point of view

#### Outline

- Assumptions underlying information theory
- Convenient versus critical

- Heart of the matter
- Long sequences of random variables are "easy" to predict (weak law, AEP)
- This piece current takes 3.5 lectures * 1.5 hours = ~ 6 hours

- Example: achievability (in sketch form) of the channel coding theorem
- Can probably be done in 1-2 lectures of 1.5 hours each

- Long sequences of random variables are "easy" to predict (weak law, AEP)

- Entropy will have be introduced, but probably not entropy rate
- Should be enough to touch on Bode/Shannon pictures
- Should also be able to talk about stochastic versus worst case

## = Tracey

#### Error correction coding

- Coverage

- High level concetnrs, framework, assumptions
- Connections with other fields
- Details of a few illustrative results

- Avoid excessive dpulication of material covered in EE 127, 127

- Want to impart a basic knowledge of what are some connections between them and other fields, so that students will have a basis for deciding if they want to go deeper

### = Topics

- Framework and assumptions (1 hr)
- Differences between information theory and coding theory
- Differences between stoachastic and adversarial noise
- Block length, complexity, etc (coding theory works with constraints, etc)

- Upper bounds on codes (2 hr)
- Classes of codes: random codes, algebraic coes, sparse graph codes (2-3 hr)
- Decoding techniques (algebraic, sum product algorithm aand special cases, LP decoding) (2-3hr)
- Networking coding and its relation to network information theory, coding thoery and networking optimization (2-3 hr)
- Connections with other fields (learning, cryptography) (2-3 hr)