Information and Coding Theory
A.Y. 2018/2019
Learning objectives
Il corso si prefigge di fornire agli studenti un'approfondita conoscenza della teoria dell'informazione secondo Claude Shannon, insieme alle necessarie nozioni di teoria dei codici
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
Second semester
Course syllabus
Course outline: Introduction, Shannon's theorem (source coding and noisy channel coding), entropy and its properties, joint and conditional entropy, mutual information, block coding, uniquely decodable codes, Huffman code, Shannon-Fano Code, Kraft inequality, Optimal codes, bounds of the optimal codes length, channel capacity and its properties, binary symmetric channel, noise/noisless channel, channel coding theorem, linear code and its properties, Hamming codes, syndrome, Hamming distance, Hamming weight, error detection, error correction, cyclic code and its properties, parity matrix H, Reed-Solomon codes, BCH codes, convolutional codes, turbo codes, LDPC/MDPC codes.
Professor(s)
Reception:
By email appointment
Room 5008, 5th Floor, via Celoria 18, Computer Science Department