Course Identification

Introduction to Signal Sampling and Processing

Lecturers and Teaching Assistants

Prof. Yonina Eldar
Yhonatan Kvich

Course Schedule and Location

First Semester
Sunday, 16:15 - 18:00, Ziskind, Rm 155

Sunday, 15:15 - 16:00,

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 4.00 points
Chemical Sciences: Lecture; Elective; 4.00 points
Life Sciences: Lecture; 4.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; 4.00 points


This course will be held by hybrid learning.
Tutorial at Ziskind, Rm 1





Language of Instruction


Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)


Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



In this course we will cover basic concepts underlying sampling and processing of signals including model-based deep learning for signal processing. The course will be roughly divided into two parts where the first part will focus on signal sampling and model-based deep learning for recovery, and the second half will focus on the fundamentals of statistical signal processing.

Tentative list of topics:

  1. Introduction and Nyquist rate sampling
  2. Sampling in shift-invariant subspaces
  3. Introduction to compressed sensing
  4. Sub-Nyquist sampling of multiband signals
  5. Finite rate of innovation sampling
  6. Applications to radar and ultrasound
  7. Model-based deep learning for signal recovery
  8. Random variables: Joint and conditional distribution, MSE estimation
  9. Stochastic processes: Stationarity and ergodicity, spectral density
  10. Filtering of random signals: LTI systems and stationarity, sampling of stationary signals
  11. Estimation of random signals: Wiener filtering, adaptive filters
  12. Digital communications in a nutshell: MAP decoding, Viterbi detection
  13. Parameter estimation 1: bias-variance tradeoff, Fisher information
  14. Parameter estimation 2: Cramer-Rao bound, maximum likelihood.


Learning Outcomes

Upon successful completion of this course, the students should understand theoretically and be able to apply signal processing tools in various relevant scenarios, ranging from generic signal acquisition to medical imaging and digital communications, including model-based deep learning.

In addition, the student should be able to adapt these concepts when tackling new unexplored challenges involving the acquisition, processing, and inference of signals.

Reading List

Y. C. Eldar Sampling theory: Beyond Bandlimited Systems. Cambridge University Press, 2015.

Y. C. Eldar, G. Kutyniok, Compressed Sensing: Theory and Applications. Cambridge University Press, 2012.

. Papoulis, Probability, Random Variables and Stochastic Processes, 3rd Ed., McGraw-
Hill, 1991. 

S. M. Kay, Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory,
Prentice-Hall, 1993 

H. L. Van Trees, Detection, Estimation and Modulation Theory, Part I, John Wiley & Sons,