Data Compression and Entropy: Basics and Introduction

This lecture covers the fundamental concepts of data compression and entropy, addressing questions on representing physical reality with bits, restoring reality from bits, measuring information quantity in data, and the importance of data compression. It explains the types of data that can be compressed, the principles behind data compression, and the distinction between lossless and lossy compression. The lecture also delves into the concept of redundancy in data and language, emphasizing the significance of compression in reducing memory space and transmission time. Additionally, it outlines the detailed plan for upcoming lessons, including entropy, lossless compression algorithms like Shannon-Fano, and optimal compression techniques like Huffman coding.