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ECS 171 > Midterm 1 > Flashcards

Midterm 1 Flashcards

1
Q

What is Machine Learning?

A
  • A training set has attributes, where one of the attributes is the class.
  • We want to find a model for class attribute as some function of the values of other attributes using a test set.
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2
Q

The dataset is split into?

A

a training and testing set

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3
Q

Confusion Matrix

A

A way to lay out how many predicted categories/classes were correctly predicted and how many were not.
- true positive, true negative, false positives, false negatives

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4
Q

Which of the following statements is/are correct?

a) In machine learning, most of the data is used for testing.
b) In machine learning, most of the data is used for training.
c) Training set is used to determine the accuracy of the model.
d) b and c.

A

b) In machine learning, most of the data is used for training.

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5
Q

which machine learning technique should be applied to the following problem?
“In information retrieval, a search engine needs to find groups of documents that
are similar to each other based on important term appearing in them”.
a) Clustering
b) Classification
c) Regression
d) Validation

A

a) Clustering

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6
Q

Which of the following tasks is an unsupervised learning technique?

a) Clustering
b) Classification
c) Regression
d) All of the above

A

a) Clustering

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7
Q

Which of the following methods requires having a training set and test set?

a) Supervised Learning
b) Unsupervised Learning
c) a and b
d) None of the above

A

a) Supervised Learning

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8
Q

Which of the following is NOT an example of a machine learning problem?
a) Optical character recognition: categorize images of handwritten characters by
letters represented
b) Face detection: find faces in images
c) Topic spotting: categorize news articles
d) None of the above.

A

d) None of the above.

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9
Q

In classification problems, there may be multiple ways of classifying data items,
i.e., a data item may belong to more than one classification category. T/F

A

T

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10
Q

Which of the following is an example of a flag variable?

a) Gender: female/male
b) Weather: clear/rainy/cloudy
c) Temperature: [21, 80]
d) a and b

A

d) a and b

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11
Q

K-means clustering is an unsupervised technique to partition the dataset into K
pre-defined distinct non-overlapping subgroups. T/F

A

T

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12
Q

Association rules are good means to predict sequential dependencies among
different events. T/F

A

T

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13
Q

Why do we use regularization on models?

a) To measure the accuracy of a model
b) To prevent overfitting
c) To train a model
d) All of the above

A

b) To prevent overfitting

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14
Q

What does the loss function measure?

a) residual error
b) prediction error
c) model parameters
d) all of the above

A

b) prediction error

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15
Q

Training set is used to determine the accuracy of the model. T/F

A

F

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16
Q

In a 2-layered Neural Network, the perceptron takes an input, calculates the weighted
sum of the inputs and weights, and returns 1 if the weighted sum is above a threshold
value (T/F)

A

T

17
Q

When training a model, the main goal is to:

a) Update model coefficients
b) Minimize the error by updating model coefficients
c) Add bias
d) None of the above

A

b) Minimize the error by updating model coefficients

18
Q

N-fold Cross validation is a method used to prevent overfitting. T/F

A

T

19
Q

OLS method is used when the relationship between input and output is very complex.
T/F

A

F

20
Q

What is Ordinary Least Squares Method for?

a) Minimize the loss function
b) Maximize the loss function
c) Update the parameters of a model
d) a and c

A

d) a and c

21
Q

Why do we use regularization on models?

a) To measure the accuracy of a model
b) To prevent overfitting
c) To train a model
d) All of the above

A

b) To prevent overfitting

22
Q

Gradient Descent method is used when the relationship between input and output is very
complex. T/F

A

T

23
Q

Regularization is a method that penalizes model coefficients to reduce overfitting.
T/F

A

T

24
Q

Lasso is an example of regularization method. T/F

A

T

25
Q

what is the name of a 3-layered neural network?

a) Perceptron
b) Multilayer Perceptron
c) Deep Neural Network
d) None of the above

A

b) Multilayer Perceptron

26
Q

What is the popular technique to find the parameters of a Deep neural network?

a) OLS
b) Stochastic gradient descent
c) Mini-batch gradient descent
d) None of the above

A

c) Mini-batch gradient descent

27
Q 
What is the popular technique to find the parameters of a shallow neural 
network? 
a)  OLS 
b)  Stochastic gradient descent 
c)  Mini-batch gradient descent 
d)  None of the above
A

b) Stochastic gradient descent

28
Q

How many output neurons in ANN is needed to perform a binary classification?

a) 1
b) 2
c) 3
d) 4

A

a) 1

29
Q 
how many output neurons in ANN is needed to perform multiclass classification 
when the output labels are ordered? 
a)  1 
b)  2 
c)  3 
d)  4
A

a) 1

30
Q

In a deep neural network, different activation functions may be used at different
layers. T/F

A

T

31
Q

A perceptron model can be used to emulate the functionality of AND logical gate.
T/F

A

T

32
Q

A 2-layered Neural network can be used to emulate the functionality of XOR
gate. T/F

A

F

33
Q

when is 1-of-n output encoding implemented in an Artificial Neural Network?

a) To perform binary classification
b) To perform multiclass classification when output variables are ordered
c) To perform multiclass classification then the output variables are not ordered
d) All the above

A

c) To perform multiclass classification then the output variables are not ordered

34
Q

when is 1-of-n output encoding implemented in an Artificial Neural Network?

a) To perform binary classification
b) To perform multiclass classification when output variables are ordered
c) To perform multiclass classification then the output variables are not ordered
d) All the above

A

c) To perform multiclass classification then the output variables are not ordered

35
Q

MLE uses natural log to optimize the computation cost of the MLE. T/F

A

T

36
Q

Given a dataset with x1,…, x6 input attributes, how are the terms in a polynomial
regression model constructed for this dataset?
a) Features of the dataset are converted to their higher order polynomial to
represent the terms in the model
b) Features of the dataset are used in the same way as linear regression model
c) Always two features are used
d) None of the above

A

a) Features of the dataset are converted to their higher order polynomial to
represent the terms in the model

37
Q

What does the loss function measure?

a) residual error
b) prediction error
c) model parameters
d) all of the above

A

b) prediction error

38
Q

K-means clustering is an unsupervised technique to partition the dataset into K
pre-defined distinct non-overlapping subgroups. T/F

A

T

ECS 171 (2 decks)

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