In this lecture we will discuss the various methods to transform information in a representation suited for computers and how to turn this information into knowledge.
We will start with the foundational questions of what knowledge even is and advance towards various way of encoding it. We discuss how the degree of similarity between pieces of information can be determined to answer questions such as “is the color black similar to white?” by using various human-curated resources.
In the subfield of machine learning we explore how the computer learns to recognize objects in the real world either from human-prepared data or all on its own. We will discuss several machine learning algorithm that are commonly used today and highlight some of their properties that one should consider when choosing one algorithm.