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.