0 [{"id":414531,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:29:03","updated_at":"2018-03-23 14:45:18","questionName":"(6)The feasible set in an optimisation problem is:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":13,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":368048,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-01-28 16:46:45","updated_at":"2018-03-23 14:45:18","questionName":"(4)x.y is zero if and only if: (. is dot) ( x &y are bold)","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":12,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414527,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:18:56","updated_at":"2018-03-23 14:45:17","questionName":"(5)The determinant of a matrix is equal to:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":3,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":368047,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-01-28 16:43:53","updated_at":"2018-03-23 14:45:17","questionName":"(4)Which of these operations is not defined over vectors in a topological vector space equipped with an inner product?","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":11,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414522,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 13:51:06","updated_at":"2018-03-23 14:43:45","questionName":"(5)Applying the linear map defined by the matrix A to the column vector x should be written:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":0,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414532,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:33:38","updated_at":"2018-03-23 14:45:17","questionName":"(6)In an approximation problem, we'd often have a loss function of the form:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":6,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414529,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:23:17","updated_at":"2018-03-23 14:45:17","questionName":"(6)A convex constraint is equivalent to a restriction to a portion of the parameter space:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":1,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":368049,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-01-28 16:49:19","updated_at":"2018-03-23 14:45:17","questionName":"(4)Distances in high-dimension can be counter intuitive because:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":7,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414524,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:14:58","updated_at":"2018-03-23 14:45:17","questionName":"(5)if A is orthogonal, then:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":5,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414528,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:22:35","updated_at":"2018-03-23 14:45:17","questionName":"(6)I want to find the shape of an object, with constant surface area, that holds the least water. What is the objective function?","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":4,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414533,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:41:28","updated_at":"2018-03-23 14:45:17","questionName":"(6)The definition of an eigenvector is:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":10,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414530,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:27:00","updated_at":"2018-03-23 14:43:45","questionName":"(6)An objective function is nonconvex, if:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":8,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414523,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:13:05","updated_at":"2018-03-23 14:45:17","questionName":"(5)Repeatedly applying a matrix A to a random initial vector x0, normalising after each step, will lead to:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":2,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":368050,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-01-28 16:51:02","updated_at":"2018-03-23 14:45:18","questionName":"(4)The covariance matrix represents:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":14,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null},{"id":414526,"quiz_id":"18612","answer_id":null,"answerType_id":"0","created_at":"2018-03-23 14:17:13","updated_at":"2018-03-23 14:45:17","questionName":"(5)In a stochastic matrix:","questionTimeSeconds":"0","questionTimeMinutes":"2","questionImagePath":null,"position":9,"explanation":"","question_score_id":null,"lang":null,"questionAudioPath":null}]

block

(6)The feasible set in an optimisation problem is:

Select the correct answer(s).  

(0/0)

---

The most distant configurations in the parameter space.

The possible values of the objective function.

The best solutions to the problem.

The possible configuration of the parameters.

a kind of metaheuristic

none

(4)x.y is zero if and only if: (. is dot) ( x &y are bold)

Select the correct answer(s).  

(0/0)

---

x is orthogonal to y

x is not equal to y

x is equal to y

x is nonzero

x is a scaled version of y

none

(5)The determinant of a matrix is equal to:

Select the correct answer(s).  

(0/0)

---

The product of the eigenvalues

The product of the rows

The sum of the diagonal

The sum of the eigenvalues

The Frobenious norm of the matrix

none

(4)Which of these operations is not defined over vectors in a topological vector space equipped with an inner product?

Select the correct answer(s).  

(0/0)

---

square root

length measurement

inner product

scalar multiplication

addition

none

(5)Applying the linear map defined by the matrix A to the column vector x should be written:

Select the correct answer(s).  

(0/0)

---

Ax^T

Ax

x^(T) Ax

xA

xAx^T

none

(6)In an approximation problem, we'd often have a loss function of the form:

Select the correct answer(s).  

(0/0)

---

L(theta) = || f(x;(theta)-y) || #x is a vector

L(theta) = 0/f(x-(theta)) #x is a vector

L(theta) = 1/(theta)

L(theta) = || (theta)-x || //x is a vector

L(theta) = (theta)x #x is a vector

none

(6)A convex constraint is equivalent to a restriction to a portion of the parameter space:

Select the correct answer(s).  

(0/0)

---

defined by a collection of planes.

within a torus of fixed radius

inside an axis-aligned box.

where the minima are.

where the parameter vector has a fixed L(inf) norm.

none

(4)Distances in high-dimension can be counter intuitive because:

Select the correct answer(s).  

(0/0)

---

Distances will span a huge range of possible values

Every vector will have a very similar distance to every other vector

There are so many different kinds of distances

Only the L∞ norm can be applied

Distances cannot be computed

none

(5)if A is orthogonal, then:

Select the correct answer(s).  

(0/0)

---

A = A^(-1)

A^T = A

A^(-1) = A^T

A^A = T^A

A = AA^T

none

(6)I want to find the shape of an object, with constant surface area, that holds the least water. What is the objective function?

Select the correct answer(s).  

(0/0)

---

The amount of water the object holds.

The shape of the object

The surface area of the object

The colour of the surface

None of the above

none

(6)The definition of an eigenvector is:

Select the correct answer(s).  

(0/0)

---

A^(-1)

A

A

Aλ = ||

λ = ||

none

(6)An objective function is nonconvex, if:

Select the correct answer(s).  

(0/0)

---

It is incomputable.

It is discontinuous

It has two maxima.

It more than one minimum

It is partially differentiable

none

(5)Repeatedly applying a matrix A to a random initial vector x0, normalising after each step, will lead to:

Select the correct answer(s).  

(0/0)

---

the cross eigenvector

the leading eigenvector

the minor eigenvector

zero

infinity? (not too sure)

none

(4)The covariance matrix represents:

Select the correct answer(s).  

(0/0)

---

the colour of the dataset

the cross product of the mean vector with itself

the number of non-zero elements in a dataset

the size of the largest element of a dataset

The spread of the dataset around its mean

none

(5)In a stochastic matrix:

Select the correct answer(s).  

(0/0)

---

The sum of all elements 1

all elements are either 0 or 1

The sum of all elements -1

The sum of each row is 1

The sum of each row is 0