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Department of Computer Science and Technology

Information Theory

Course pages 2020–21

Information Theory

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




Total duration: 15:00:31