Coursera

Question 1

machine learning (older definition)

Answer 1

the science of getting computers to learn, without being explicitly programmed

Question 2

supervised learning

Answer 2

given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output

Question 3

regression problem

Answer 3

trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function (eg house price based on square feet)

Question 4

classification problem

Answer 4

trying to predict results in a discrete output (eg breast tumor is malignant or benign)

Question 5

unsupervised learning

Answer 5

allows us to approach problems with little or no idea what our results should look like

Question 6

clustering algorithm

Answer 6

eg. Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.

Question 7

machine learning (modern definition)

Answer 7

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Question 8

cost function

Answer 8

This function is otherwise called the “Squared error function”, or Mean squared error.

Question 9

gradient descent equation

Answer 9

Question 10

course notation

Answer 10

m: number of training examples x’s = “input” variable/features y’s = “output” variable/”target” variable

Question 11

gradient descent for linear regression equation

Answer 11

Question 12

matrix

Answer 12

rectangular array of numbers written between square brackets; in other words a 2-D or 2-dimensional array

Question 13

dimension of a matrix

Answer 13

The number of rows in a matrix X the number of columns in a matrix.

Question 14

If A is a matrix, what is A_ij referring to?

Answer 14

The entry in the i^th row and j^th column

Question 15

vector

Answer 15

A vector is an n x 1 matrix; in other words it is a special case of a matrix where there is only 1 column

Question 16

If y is a vector, what is y_i referring to?

Answer 16

The entry in the i^th row. Note that there are two ways of indexing vectors, starting at 1 (the more common in math) or starting at 0 (used in some applications for machine learning).

Question 17

scalar multiplication (matrix)

Answer 17

Multiplying a matrix by a number.

Question 18

Is matrix multiplication commutative?

Answer 18

No

Question 19

Is matrix multiplication associative?

Answer 19

Yes

Question 20

What is the identity matrix?

Answer 20

Denoted by I or I_nxn

For any matrix A, A • I = I • A = A

Question 21

What is a matrix that does not have an inverse called?

Answer 21

Singular or degenerate

Question 22

matrix inverse operation

Answer 22

tbd

Question 23

matrix transpose

Answer 23

A^T_ij = A_ji

Question 24

x_j⁽ⁱ⁾

Answer 24

value of feature j in the i^th training example

Question 25

x⁽ⁱ⁾ in multivariable linear regression

Answer 25

the column vector of all the feature inputs of the i^th training example