Lecture 1: The Learning Problem

1.Course Introduction -- foundation oriented and story-like

Foundation oriented ML course.

2.What is Machine Learning -- use data to approximate target

Machine learning

Key essence of machine learning

3.Applications of Machine Learning -- almost everywhere

Food, clothing, housing, transportation, education, entertainment, ...

4.Components of Machine Learning -- takes and to get

Basic notations

The learning model

learning model = and

Another definition

5.Machine Learning and Other Fields -- related to DM, AI and Stats

machine learning: use data to compute hypothesis g that approximates target f.
data mining: use (huge) data to find property that is interesting.
artificial intelligence: compute something that shows intelligent behavior.
statistics: use data to make inference about an unknown process.

ML and DM

difficult to distinguish ML and DM in reality.

ML and AI

ML is one possible route to realize AI.

ML and Stats

statistics: many useful tools for ML.