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Created by:
Last updated:
August 6, 2022
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
Description
This is a graduate-level introduction to mathematics of information theory.
We will cover both classical and modern topics, including information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression.
Course Curriculum
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- Chapter 1: Information measures: Entropy and divergence Unlimited
- Chapter 2: Information measures: Mutual information Unlimited
- Chapter 3: Sufficient statistic. Continuity of divergence and mutual information. Unlimited
- Chapter 4: Extremization of mutual information: Capacity saddle point Unlimited
- Chapter 5: Single-letterization. Probability of error. Entropy rate. Unlimited
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- Chapter 6: Variable-length Lossless Compression Unlimited
- Chapter 7: Fixed-length (almost lossless) compression. Slepian-Wolf problem Unlimited
- Chapter 8: Compressing stationary ergodic sources Unlimited
- Chapter 9: Universal compression Unlimited
- Chapter 10: Binary hypothesis testing Unlimited
- Chapter 11: Hypothesis testing asymptotics I Unlimited
- Chapter 12: Information projection and Large deviation Unlimited
- Chapter 13: Hypothesis testing asymptotics II Unlimited
- Chapter 23: Rate-distortion theory Unlimited
- Chapter 24: Rate distortion: Achievability bounds Unlimited
- Chapter 25: Evaluating R(D). Lossy Source-Channel separation. Unlimited
About the instructor
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Massachusetts Institute of Technology
