Information and Coding Theory
A.Y. 2025/2026
Learning objectives
This course will set out the fundamental concepts of information theory according to Claude Shannon. In addition, the main notions of coding theory will be presented.
Expected learning outcomes
At the end of the course the students will be able to: (1) describe and model information sources and (2) transmit C bits of information over a (noisy) channel.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Introduction, Model and basic operations of information processing systems, Information source, Encoding a source alphabet, Octal and hexadecimal codes, The ASCII code, Error-detecting codes, White noise, Single parity-check code, Burst error-detecting code, Trade-off between redundancy and error-detecting capability, Repetition codes, Hamming codes, Data compression, Instantaneous codes, The Kraft Inequality, Huffman code, Shannon's theorem (source coding and noisy channel coding), Entropy and its properties, Mutual information, Channel capacity and its properties, Approaching the Shannon limit, BCH, Reed-Solomon codes, Cyclic codes, Quantum-resistant codes.
Prerequisites for admission
A basic knowledge of statistics and discrete mathematics would be helpful in understanding the concepts taught in this course.
Teaching methods
Classroom Lectures: Although attendance is not compulsory, it is strongly recommended.
Teaching Resources
Home page: https://aviscontic1.ariel.ctu.unimi.it/, https://visconti.di.unimi.it/
Textbooks:
* STEFAN M. MOSER, PO-NING CHEN, A Student's Guide to Coding and Information Theory, Cambridge University Press
* Thomas Cover, Joy Thomas, Elements of Information Theory, Wiley
* Jiri Adamek, Foundations of Coding, Wiley
* Richard Hamming, Coding and Information Theory, Prentice-Hall
Papers, slides, and additional resources (if any) can be found on the course page.
Textbooks:
* STEFAN M. MOSER, PO-NING CHEN, A Student's Guide to Coding and Information Theory, Cambridge University Press
* Thomas Cover, Joy Thomas, Elements of Information Theory, Wiley
* Jiri Adamek, Foundations of Coding, Wiley
* Richard Hamming, Coding and Information Theory, Prentice-Hall
Papers, slides, and additional resources (if any) can be found on the course page.
Assessment methods and Criteria
Oral Exam: To pass the exam, students must demonstrate sufficient knowledge of the subject, including information theory, coding theory, algorithms, proofs, and exercises. Scores will range from 0 to 30.
Professor(s)
Reception:
By email appointment
Room 5008, 5th Floor, via Celoria 18, Computer Science Department