### Information and Coding Theory (EE 410)

2021 Spring
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Özgür Erçetin oercetin@sabanciuniv.edu,
English
MATH203 MATH201
Formal lecture
Interactive,Communicative

### CONTENT

Mathematical models for communication channels and sources; entropy, information, lossless data compression, Huffman coding, channel capacity, Shannon's theorems rate-distortion theory.

### OBJECTIVE

To learn about information, how to measure it and how to use it to better design information systems.

### LEARNING OUTCOME

Define the information content of an information source mathematically and define information theoretical measures such as entropy, conditional entropy, joint entropy mutual information, differential entropy etc.
Describe the fundamental limit in source coding and learn Shannon?s Source Coding Theorem
Design and implement some of the practical source codes
Describe the fundamental limit in maximum information rate at which the information is sent reliably and learn Shannon?s Channel Capacity Theorem.
Design and implement some of the practical channel codes
Describe the capacity of Gaussian Channel and optimal power allocation over Gaussian Channel using Water-Filling algorithm.
Describe the application of information theory to some of engineering problems through the course project.

### ASSESSMENT METHODS and CRITERIA

 Percentage (%) Final 40 Midterm 40 Exam 10 Participation 10