The Learning Problem: When Machine Learning Is the Right Tool | NTU Hsuan-Tien Lin Machine Learning Foundations Lecture 1
Foundations Lecture 1 defines machine learning as "use data to compute a hypothesis g that approximates the target f" and gives three conditions for deciding whether to use it: there is a pattern to learn, there is no programmable definition, and there is data. The skeleton of the whole lecture is one flow chart: an unknown f generates the data D, and an algorithm A picks g from a hypothesis set H. A plus H is called the learning model, and the next 15 lectures keep swapping parts on this chart.