Information Theory
Covers information measures, channel capacity for discrete/continuous noisy channels, coding theorems, and source/channel coding fundamentals.
Summary
Recent recorded grades — Fall 2018: 3.30 GPA, 68.2% A/AB (n=22 letter grades); Fall 2019: 3.43 GPA, 78.6% A/AB (n=14 letter grades); Spring 2022: 3.71 GPA, 75.0% A/AB (n=12 letter grades).
What you’ll be able to do
Calculate the entropy of a random variable from its distribution, the mutual information between two random variables from their joint distribution, the Kullback-Leibler divergence between two probability distributions, and the entropy rate of a Markov process
Construct source codes such as the Huffman code, the Shannon code, and the Elias-Fano code for a given probability distribution of the source
Define channel capacity and apply Shannon’s channel coding theorem to Calculate the channel capacities of discrete channels such as the binary symmetric channel and the binary erasure channel, and continuous channels such as the additive Gaussian noise channel
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Recent recorded grades — Fall 2018: 3.30 GPA, 68.2% A/AB (n=22 letter grades); Fall 2019: 3.43 GPA, 78.6% A/AB (n=14 letter grades); Spring 2022: 3.71 GPA, 75.0% A/AB (n=12 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Measures of information and their properties
- Capacity of discrete and continuous channels with noise
- Source and channel coding theorems
- Fundamentals of channel coding, noiseless source coding, and source coding with fidelity criteria
Skills
- Calculate entropy, mutual information, Kullback-Leibler divergence, and entropy rate of a Markov process
- Construct source codes including Huffman, Shannon, and Elias-Fano codes
- Define channel capacity and calculate capacities for discrete and continuous channels
Grades
Latest available · Spring 2022— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2022
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
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Raw records
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Student reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}