EE 276: Course Outline

Lecture 1, Jan 9: Introductory lecture.

• [slides]

Lecture 2, Jan 11: Information measures: entropy (joint, relative and condition).

• [notes]

• Similar in coverage to 2020 Video Lecture 2 (on canvas).

Lecture 3, Jan 16: Asymptotic Equipartition Property (AEP) and typicality.

• [notes]

• Similar in coverage to 2020 Video Lecture 3 (on canvas).

Lecture 4, Jan 18: Variable length lossless compression: prefix and Shannon codes

• [notes]

Lecture 5, Jan 23: The Kraft-McMillan inequality and Huffman coding

• [notes]

Lecture 6, Jan 25: Entropy rates and universal compression

• Lecture video: 1/23 of Winter 2020 EE 276 on Canvas.

Lecture 7, Jan 30: Reliable communication and channel capacity

• [notes]

Lecture 8, Feb 1: Information measures for continuous random variables

• [notes]

Lecture 9, Feb 6: AWGN channel

• [notes]

Lecture 10, Feb 8: Channel coding theorem converse

• [notes]

Lecture 11, Feb 13: Joint AEP and channel coding theorem

• [notes]

Lecture 12, Feb 15: Polar codes

• 2017 notes [L1],[L2 ]

• [Slides from lecture]

• [Slides on decoding]

Lecture 13, Feb 20: Lossy Compression and Rate Distortion

• [notes]

Lecture 14, Feb 22: Lossy Compression and Rate Distortion Continued

• [notes]

Lecture 15, Feb 27: Method of Types

• [notes]

Lecture 16, Feb 29: Strong, conditional and joint typicality

• [notes]

Lecture 17, Mar 5: Joint source-channel coding and the separation theorem

• [notes]

Lecture 18, Mar 7: Joint source-channel coding and the separation theorem 2

• [notes]