Information Theory: Principles of Data and Communication

In an age defined by data, understanding the fundamental limits of communication and information processing is essential for anyone working in technology, data science, or engineering. This course provides a solid grounding in the mathematical theory of communication, enabling you to analyze how information is quantified and how it flows through various systems. You will gain a comprehensive understanding of the laws governing data, from the simplest source codes to the complexities of network transmission. By the end of this course, you will be able to evaluate communication efficiency and understand the theoretical boundaries of what is possible in data storage and transfer. What you'll learn: - Understand the core concepts of entropy and mutual information to measure information content. - Apply source coding theorems to determine the theoretical limits of data compression. - Analyze channel capacity and the principles of reliable communication over noisy channels. - Practice calculations for foundational data representation techniques and coding schemes. - Explore the relationship between information theory and modern fields like machine learning and network coding. - Master the fundamental inequalities that govern information flow and data processing. The course begins with essential definitions and mathematical prerequisites, establishing a firm conceptual base before moving into source coding, channel capacity, and the principles of multi-user information theory. The material is presented through clear written explanations and detailed examples to ensure a deep grasp of the logic behind the math. This course is designed for beginners in the field, including students and professionals in computer science, engineering, or mathematics who possess a basic understanding of probability. No prior experience with information theory is required. Start building your foundational knowledge of the laws that govern the digital world today.