This lecture is an introduction to Shannon's information theory. The essence of information theory is "what is information mathematically?". We will consider a mathematical model of "amount of information", "coding of information" etc and study their basic theory. In transmission and recording, we want to reduce the amount of data. On the other hand, in communication, there is a possibility of transmission error, and coding is required to minimize the transmission error. We will consider the basic idea and method for these problems.
Information theory is a basic theory of expression and transmission of "information". Typical applications include data compression, bit error detection / correction, and encryption. Information theory also plays an important role in machine learning algorithms (for example, cross-entropy can be adopted as an objective function in neural networks).
The goal is to learn the quantitative treatment of "information" that is used in our daily life.