skip to primary navigation skip to content

Information Theory

Contents

1 Foundations
2 Entropies defined
3 Distances and Markov sources
4 Variable-length and Huffman codes
5 Discrete channel capacity and error-correcting codes
6 Information projections into vector spaces
7 Fourier representations of information
8 Spectral properties, noise, and capacity of continuous channels
9 Analytical tools for aperiodic signals and data
10 Continuous-time encodings and demodulation schemes
11 Quantised degrees-of-freedom in continuous signals
12 Information diagram, Uncertainty Principle, and Gabor wavelets
13 Discrete, and Fast Fourier Transforms with butterfly algorithm
14 Analysis by wavelets, and image compression protocols
15 Kolmogorov complexity, astrophysics, and genomics
16 Applications of information theory in neuroscience and pattern recognition
Examples-Class-1
Examples-Class-2
Examples-Class-3

1 Foundations

2 Entropies defined

3 Distances and Markov sources

4 Variable-length and Huffman codes

5 Discrete channel capacity and error-correcting codes

6 Information projections into vector spaces

7 Fourier representations of information

8 Spectral properties, noise, and capacity of continuous channels

9 Analytical tools for aperiodic signals and data

10 Continuous-time encodings and demodulation schemes

11 Quantised degrees-of-freedom in continuous signals

12 Information diagram, Uncertainty Principle, and Gabor wavelets

13 Discrete, and Fast Fourier Transforms with butterfly algorithm

14 Analysis by wavelets, and image compression protocols

15 Kolmogorov complexity, astrophysics, and genomics

16 Applications of information theory in neuroscience and pattern recognition

Examples-Class-1

Examples-Class-2

Examples-Class-3

Total duration: 15:00:31