Introductory Concepts

Question 1

Machine Learning

Answer 1

constructs algorithms that can learn from

data.

Question 2

Statistical Learning

Answer 2

is a branch of applied statistics that
emerged in response to machine learning,
emphasizing statistical models and assessment of
uncertainty.

Question 3

Data Science

Answer 3

is the extraction of knowledge from data, using
ideas from mathematics, statistics, machine
learning, computer science, engineering,

Question 4

Wide Data

Answer 4

We have too many variables; prone to overfitting.

Need to remove variables, or regularize, or both.

Question 5

Tall Data

Answer 5

Sometimes simple models (linear) don’t suffice.
We have enough samples to fit nonlinear models with many interactions, and not too many variables.
Good automatic methods for doing this.

Question 6

Linear regression

Answer 6

Linear regression is a simple approach to supervise
learning. It assumes that the dependence of Y on
X1;X2; : : :Xp is linear.

Question 7

Mean Errror Model Training

Answer 7

MSETr = Avei2Tr[yi - ^ f(xi)]2

Question 8

Mean Error Model Test

Answer 8

MSETe = Avei2Te[yi - ^ f(xi)]2

Question 9

Bias-Variance Trade-off

Answer 9

E(y0 - ^ f(x0))2

= Var( ^ f(x0)) + [Bias( ^ f(x0))]2 + Var(e)

Question 10

RSS

Answer 10

Residual sum square

Question 11

E(y0 - ^ f(x0))2

Answer 11

Mean square error

Question 12

Var( ^ f(x0))

Answer 12

Variability connected to the estimamed function model

Question 13

[Bias( ^ f(x0))]2]

Answer 13

How far is your model from the truth