Lectures related to Multi-user Gaussian Channels with Noisy Feedback

Information Theory: Source Coding, Cryptography, Channel Coding

Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.

Lecture: Shannon

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Introduction: Course Structure and Fundamentals of Computing

Explores the role of Computing in society and the basics of computing, algorithms, communication systems, and computer security.

Channel Coding: Convolutional Codes

Explores channel coding with a focus on convolutional codes, emphasizing error detection, correction, and decoding processes.

Entropy: Examples and Properties

Explores examples of guessing letters, origins of entropy, and properties in information theory.

Introduction to Signals and Filters

Covers the conversion of analog signals to digital, data compression, and signal reconstruction, highlighting the significance of signal processing in communication systems.

Secret Key Generation: Polar Coding

Explores secret key generation using polar coding for short blocklengths, discussing key capacity, rate-leakage pairs, and practical implementation.

Information Theory: Quantifying Messages and Source Entropy

Covers quantifying information in messages, source entropy, common information, and communication channel capacity.

Information Theory: Basics and Applications

Covers the basics of information theory and its applications in various fields.

Information Theory Basics

Introduces information theory basics, including entropy, independence, and binary entropy function.

Introduction to Information, Computation, and Communication

Introduces the fundamental principles of Information, Computation, and Communication theory, covering genomics, medical imaging, and assistive technology.

Achievable Rate & Capacity

Explores achievable rate, channel capacity, spectral efficiency, and fading channels in wireless communication systems.

Compression: introduction

Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.

Network Coding: An Instant Primer

Introduces network coding, explaining its benefits, implications, and practical applications in networking systems.

Communication Channels: Gaussian Noise and Capacity

Explores the capacity of communication channels with Gaussian noise and noise impact.

Stochastic Models for Communications: Introduction

Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.

Quantifying Information: Probability, Entropy, and Constraints

Explores quantifying information based on probability, entropy, and constraints in communication systems.

Brain Network Communication: Mechanisms and Patterns

Delves into brain network communication patterns, investigation methods, and the relationship between structural and functional connections.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.