|
|
 |
Summary of the course, January 2001
Lecture plan, suggested reading, and suggested questions
NB, these suggestions are from 2001, so don't map
perfectly onto the 2004 book. (see also supervision recommendations here)
Week 1:
Friday |
- Lecture notes:
- Ch 1 - Intro to information theory
- Exercise to do before Wednesday:
- Ex 4.2 (p.71)
- Suggested reading:
- Ch 2 - Probabilities and Inference
- Suggested examples
- Ex 1.2 (p.13), 1.8 (p.19)
|
Week 1:
Wednesday |
- Lecture notes:
- Ch 5 (now Ch 4) - Source Coding Theorem
- Suggested reading:
- rest of Ch 5 (this is the toughest bit of the course)
- Suggested examples
- Ex 2.14, 2.15, 2.18, 2.20, 2.28 (p.40)
|
| Week 2: |
- Lecture notes:
-
- Ch 6 (now Ch 5) - Symbol Codes
- Suggested examples
- Ex 6.19, 6.20, 6.25 (p.111-2)
|
| Week 3: |
- Lecture notes:
- Ch 7 (now Ch 6) - Stream Codes; (Lempel-Ziv not examinable)
- Reading :
- Ch 9 (now 8) - Correlated random variables
- Suggested examples
- Ex 7.4, 7.8 (p.131), 8.3, 8.5 (147)
Ex 9.1, 9.5, 9.7, 9.8
|
| Week 4: |
- Lecture notes:
- Ch 10-11 (now 9-10) - Communication over noisy channel; Channel coding theorem
- Suggested examples
- Ex 10.12, 10.13, 10.15
|
| Week 5: |
- Reading:
- Ch 3 - More on inference; Ch 12 (12.1-12.2) (now 11.1-11.2) - Inference for
Gaussian channels
- Suggested examples
- Ex 3.3, 10.19, 10.20, 11.4
- Lecture notes:
- Ch's 24, 25*, 27* (now 29, 30, 32) - Monte Carlo methods
|
| Week 6: |
- Lecture notes:
- Ch 28 (now 33) - Variational methods
- Reading:
- Ch 13 (now 12) - Hash codes, efficient information retrieval;
Ch 22 - Inference;
Ch 26 (now 31) - Ising models.
(Ch 13 is not examinable,
but I want you to think about the question `how to make
a content-addressable memory?')
- Suggested examples
- 24.5, 24.9, 26.3 (p. 363).
|
| Week 7: |
- Lecture notes:
- Ch 31, 32, 34 (38, 39, 41) - Neuron.
- Reading:
- Ch 33 (details optional)
- Suggested examples
- 28.2, 30.1 (p. 383). 32.2 (402), 32.5 (407)
|
| Week 8: |
- Lecture notes:
- Ch 35, 36 (now 42, 43) - Hopfield networks and Boltzmann machines.
- Suggested examples
- Automatic clustering: 22.3 (p.304); 35.3, 35.4 (441)
|
|
Site last modified Thu Sep 30 20:34:34 BST 2004
|
|
