Teaching

Linear Systems and Signal Processing (Jul-Dec 2024)

This course is an introduction to Fourier domain techniques for analysing continuous-time and discret-time signals and linear time-invariant (LTI) systems. The following topics will be covered: Fourier series, Fourier transform (continuous- and discrete-time), Discrete Fourier Transform, LTI systems, and (if time permits) Laplace and Z-transforms.

The lectures will be based on the following references

• The Fourier Transform and its Applications (Lecture Notes for EE 261) by Osgood

• Fourier Analysis: An Introduction by Stein and Shakarchi

• Fourier Analysis by Körner

• Signals and Systems by Oppenheim, Willsky and Nawab

• Discrete-Time Signal Processing by Oppenheim, Schafer and Buck

• Signal Processing for Communications by Prandoni and Vetterli

Information Theory, Coding and Inference (Jan-May 2024)

This course will serve as an introduction to information theory and will highlight its connections to statistics and coding via the following problems: binary hypothesis testing, data compression, and coding for noisy channels.

The primary references for this course are

• Thomas Cover and Joy Thomas, Elements of Information Theory, Second Edition, Wiley-Interscience
• Yury Polyanskiy and Yihong Wu, Information Theory: From Coding to Learning, draft available at Yury Polyanskiy’s page.

Recent Courses

Matrix Theory (Aug-Dec 2023).