Aims and Fit of Module

The module "Signal Processing and Information Theory" aims to provide undergraduate AI students with a fundamental understanding of signal processing principles and the core concepts of information theory. It introduces basic concepts and operations of continuous and discrete signals, time-domain and frequency-domain analysis, and the application of these principles in communication systems. Additionally, the module covers essential information theory topics such as entropy, mutual information, data compression, and error control coding, equipping students with the analytical skills necessary for modern data processing and communication technologies.
This module is a crucial part of the AI curriculum, laying the groundwork for advanced courses in machine learning, neural networks, and data communication. By integrating concepts from electrical engineering, computer science, and mathematics, it provides a multidisciplinary approach that enhances problem-solving skills. The practical aspects of signal processing and coding prepare students for real-world applications in telecommunications and digital signal processing. Overall, this module not only strengthens students' theoretical knowledge and analytical capabilities but also prepares them for research and professional careers in AI and related fields.

Learning outcomes

A Analyze linear time-invariant (LTI) systems and signals in both time and frequency domains using convolution and Fourier analysis techniques.
B Quantify information measures, including entropy and mutual information, and determine the theoretical limits of communication channel capacity.
C Design digital signal processing algorithms and interpret their computational logic or architectural requirements.
D Design and evaluate the effectiveness and applications of information coding systems in communication.

Method of teaching and learning

The module will be delivered through a comprehensive series of instructional sessions. These include both two-hour lectures, which will delve deeply into the core concepts, and one-hour tutorials, which will focus on supplementary and advanced topics. Furthrmore, there are four lab sessions scheduled throughout the course. These lab sessions are designed to provide students with hands-on experience and practical opportunities to apply the knowledge they have acquired during the lectures. This combination of theoretical instruction and practical application ensures a well-rounded educational experience. Additionally, this module integrates an AI tutor to provide academic support, helping students navigate complex theoretical derivations in signal processing and information theory.