Week 2 Flashcards
1
Q
What is the assumption we have in supervised learning?
A
2
Q
What are the two types of supervised learning problems?
A
3
Q
What is the difference between linear regression in ML?
A
4
Q
What is the output of a learning problem? What is its definition?
A
5
Q
What is a cost function? What is its idea?
A
6
Q
What is the definition of the risk?
A
7
Q
When is it possible to make a good prediction of a learning problem?
A
8
Q
What is the definition of Bayes-risk?
A
9
Q
What is the definition of excess risk?
A
10
Q
Proof that a regression with quadratic cost has a function without excess risk exists.
A
11
Q
Show that a binary classification with 0-1 cost without exess risk exists.
A
