Information Theory and Signal Processing

Instructor Ruediger Urbanke
Office INR 116
Email [email protected]
Office Hours By appointment
Teaching Assistant Kirill Ivanov
Office INR 030
Admin Assistant Muriel Bardet Office INR 137
Lectures Monday 09:15 – 11:00 Room: INM200
Friday 08:15 – 10:00 Room: INM201
Exercises Friday 10:15 – 12:00 Room: INM201
Language: English
Credits : 6 ECTS

Lecture notes (PDF file)

Official Prerequisites: COM-300 Modèles stochastiques pour les communications (or equivalent)

Here is a link to official coursebook information.

Homework:
Some Homework will be graded…

Grading:
If you do not hand in your final exam your overall grade will be NA. Otherwise, your grade will be determined based on the following weighted average:
10 % for the Homework, 90 % for the Final Exam.

Special Announcements

Last year’s Final exam and Solution.

Detailed Schedule

(tentative, subject to changes)

Date Topics Covered Exercices Reading
16/9 !!! Lundi du Jeûne  – public holiday – no course !!!  
20/9 General Introduction ; Review  Probability
Exercise: Review Session (Probability)   (ET)
HW 0

Sol 0

Handout
Information Measures Chapter 2
23/9 Basic Information Measures (ET)
27/9
HW 1
30/9
04/10
  HW 1 Sol 1
Compression and Quantization Chapter 3
07/10 Compression and Quantization (ET)
11/10 Compression and Quantization
HW 2
14/10 Compression and Quantization
18/10 Compression and Quantization
  HW 2 Sol 2
Exponential Families
21/10 Exponential families ; Max Entropy problems (RU) Chapter 4
25/10 Boltzmann distribution ; Exponential families
  HW 3 Sol 3
Multi-Arm Bandits Chapter 5
28/10 Multi-armed Bandits : Explore & Exploit (RU)  
01/11 Multi-armed Bandits : UCB algorithm
HW 4 Sol 4
04/11 Multi-armed Bandits : Converse bound
08/11 Multi-armed Bandits : Variations
  HW 4 Sol 4
Distribution Estimation
11/11 Distribution Estimation ; Property Testing and Estimation (RU) Chapter 6
15/11 Distribution Estimation ; Property Testing and Estimation
HW 5 Sol 5
18/11 Distribution Estimation ; Property Testing and Estimation
22/11 Distribution Estimation ; Property Testing and Estimation
  HW 5 Sol 5
Estimation and Detection  Chapter 7
25/11 Optimum Detection and Estimation ; MMSE (MG)
29/11 Wiener Filter, LMS Adaptive Filter
HW 6 Sol 6
02/12 Parameter estimation ; Fisher information ; Cramèr-Rao bound
06/12  Information measures, Learning ang Generalization
  HW 6 Sol 6
Signal Representations Chapter 8
09/12
Review Linear Algebra (SVD, Eckart–Young) ; Fourier
13/12 Sparse Fourier ; Hilbert space perspective
HW 7 Sol 7
16/12 Time–Frequency ; Wavelets
20/12 Wavelets ; Data-adaptive Signal Representations
HW 7 Sol 7

Textbooks