question stringlengths 16 544 | subject stringclasses 1
value | choices listlengths 4 4 | answer class label 4
classes |
|---|---|---|---|
A and B are two events. If P(A, B) decreases while P(A) increases, which of the following is true? | machine_learning | [
"P(A|B) decreases",
"P(B|A) decreases",
"P(B) decreases",
"All of above"
] | 1B |
Which is true about Batch Normalization? | machine_learning | [
"After applying batch normalization, the layer’s activations will follow a standard Gaussian distribution.",
"The bias parameter of affine layers becomes redundant if a batch normalization layer follows immediately afterward.",
"The standard weight initialization must be changed when using Batch Normalization."... | 1B |
Which of the following is true of a convolution kernel? | machine_learning | [
"Convolving an image with $\\begin{bmatrix}1 & 0 & 0\\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{bmatrix}$ would not change the image",
"Convolving an image with $\\begin{bmatrix}0 & 0 & 0\\\\ 0 & 1 & 0 \\\\ 0 & 0 & 0 \\end{bmatrix}$ would not change the image",
"Convolving an image with $\\begin{bmatrix}1 & 1 & 1\\\\ 1... | 1B |
Which of the following is false? | machine_learning | [
"Semantic segmentation models predict the class of each pixel, while multiclass image classifiers predict the class of entire image.",
"A bounding box with an IoU (intersection over union) equal to $96\\%$ would likely be considered at true positive.",
"When a predicted bounding box does not correspond to any o... | 3D |
Which of the following is false? | machine_learning | [
"The following fully connected network without activation functions is linear: $g_3(g_2(g_1(x)))$, where $g_i(x) = W_i x$ and $W_i$ are matrices.",
"Leaky ReLU $\\max\\{0.01x,x\\}$ is convex.",
"A combination of ReLUs such as $ReLU(x) - ReLU(x-1)$ is convex.",
"The loss $\\log \\sigma(x)= -\\log(1+e^{-x})$ is... | 2C |
_ refers to a model that can neither model the training data nor generalize to new data. | machine_learning | [
"good fitting",
"overfitting",
"underfitting",
"all of the above"
] | 2C |
We are training fully connected network with two hidden layers to predict housing prices. Inputs are $100$-dimensional, and have several features such as the number of square feet, the median family income, etc. The first hidden layer has $1000$ activations. The second hidden layer has $10$ activations. The output is a... | machine_learning | [
"111021",
"110010",
"111110",
"110011"
] | 0A |
Statement 1| The F1 score can be especially useful for datasets with class high imbalance. Statement 2| The area under the ROC curve is one of the main metrics used to assess anomaly detectors. | machine_learning | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A |
High entropy means that the partitions in classification are | machine_learning | [
"pure",
"not pure",
"useful",
"useless"
] | 1B |
In building a linear regression model for a particular data set, you observe the coefficient of one of the features having a relatively high negative value. This suggests that | machine_learning | [
"This feature has a strong effect on the model (should be retained)",
"This feature does not have a strong effect on the model (should be ignored)",
"It is not possible to comment on the importance of this feature without additional information",
"Nothing can be determined."
] | 2C |
For polynomial regression, which one of these structural assumptions is the one that most affects the trade-off between underfitting and overfitting: | machine_learning | [
"The polynomial degree",
"Whether we learn the weights by matrix inversion or gradient descent",
"The assumed variance of the Gaussian noise",
"The use of a constant-term unit input"
] | 0A |
The K-means algorithm: | machine_learning | [
"Requires the dimension of the feature space to be no bigger than the number of samples",
"Has the smallest value of the objective function when K = 1",
"Minimizes the within class variance for a given number of clusters",
"Converges to the global optimum if and only if the initial means are chosen as some of... | 2C |
Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate? | machine_learning | [
"Decision Trees",
"Density-based clustering",
"Model-based clustering",
"K-means clustering"
] | 1B |
MLE estimates are often undesirable because | machine_learning | [
"they are biased",
"they have high variance",
"they are not consistent estimators",
"None of the above"
] | 1B |
Computational complexity of Gradient descent is, | machine_learning | [
"linear in D",
"linear in N",
"polynomial in D",
"dependent on the number of iterations"
] | 2C |
The model obtained by applying linear regression on the identified subset of features may differ from the model obtained at the end of the process of identifying the subset during | machine_learning | [
"Best-subset selection",
"Forward stepwise selection",
"Forward stage wise selection",
"All of the above"
] | 2C |
A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there? | machine_learning | [
"12",
"24",
"48",
"72"
] | 3D |
Neural networks: | machine_learning | [
"Optimize a convex objective function",
"Can only be trained with stochastic gradient descent",
"Can use a mix of different activation functions",
"None of the above"
] | 2C |
The disadvantage of Grid search is | machine_learning | [
"It can not be applied to non-differentiable functions.",
"It can not be applied to non-continuous functions.",
"It is hard to implement.",
"It runs reasonably slow for multiple linear regression."
] | 3D |
Which of the following sentence is FALSE regarding regression? | machine_learning | [
"It relates inputs to outputs.",
"It is used for prediction.",
"It may be used for interpretation.",
"It discovers causal relationships"
] | 3D |
Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting? | machine_learning | [
"Increase the amount of training data.",
"Improve the optimisation algorithm being used for error minimisation.",
"Decrease the model complexity.",
"Reduce the noise in the training data."
] | 1B |
Which of the following is/are true regarding an SVM? | machine_learning | [
"For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line.",
"In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane.",
"For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion.",
"Overfitting i... | 0A |
Which of the following is the joint probability of H, U, P, and W described by the given Bayesian Network H -> U <- P <- W? [note: as the product of the conditional probabilities] | machine_learning | [
"P(H, U, P, W) = P(H) * P(W) * P(P) * P(U)",
"P(H, U, P, W) = P(H) * P(W) * P(P | W) * P(W | H, P)",
"P(H, U, P, W) = P(H) * P(W) * P(P | W) * P(U | H, P)",
"None of the above"
] | 2C |
Adding more basis functions in a linear model, pick the most probably option: | machine_learning | [
"Decreases model bias",
"Decreases estimation bias",
"Decreases variance",
"Doesn’t affect bias and variance"
] | 0A |
Consider the Bayesian network given below. How many independent parameters would we need if we made no assumptions about independence or conditional independence H -> U <- P <- W? | machine_learning | [
"3",
"4",
"7",
"15"
] | 3D |
Another term for out-of-distribution detection is? | machine_learning | [
"anomaly detection",
"one-class detection",
"train-test mismatch robustness",
"background detection"
] | 0A |
Statement 1| We learn a classifier f by boosting weak learners h. The functional form of f’s decision boundary is the same as h’s, but with different parameters. (e.g., if h was a linear classifier, then f is also a linear classifier). Statement 2| Cross validation can be used to select the number of iterations in boos... | machine_learning | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D |
If N is the number of instances in the training dataset, nearest neighbors has a classification run time of | machine_learning | [
"O(1)",
"O( N )",
"O(log N )",
"O( N^2 )"
] | 1B |
Which of the following can only be used when training data are linearly separable? | machine_learning | [
"Linear hard-margin SVM.",
"Linear Logistic Regression.",
"Linear Soft margin SVM.",
"The centroid method."
] | 0A |
Which of the following are the spatial clustering algorithms? | machine_learning | [
"Partitioning based clustering",
"K-means clustering",
"Grid based clustering",
"All of the above"
] | 3D |
Which among the following prevents overfitting when we perform bagging? | machine_learning | [
"The use of sampling with replacement as the sampling technique",
"The use of weak classifiers",
"The use of classification algorithms which are not prone to overfitting",
"The practice of validation performed on every classifier trained"
] | 1B |
What are support vectors? | machine_learning | [
"The examples farthest from the decision boundary.",
"The only examples necessary to compute f(x) in an SVM.",
"The data centroid.",
"All the examples that have a non-zero weight αk in a SVM."
] | 1B |
Say the incidence of a disease D is about 5 cases per 100 people (i.e., P(D) = 0.05). Let Boolean random variable D mean a patient “has disease D” and let Boolean random variable TP stand for "tests positive." Tests for disease D are known to be very accurate in the sense that the probability of testing positive when y... | machine_learning | [
"0.0495",
"0.078",
"0.635",
"0.97"
] | 2C |
You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem? | machine_learning | [
"Your decision trees are too shallow.",
"You need to increase the learning rate.",
"You are overfitting.",
"None of the above."
] | 0A |
Statement 1| Industrial-scale neural networks are normally trained on CPUs, not GPUs. Statement 2| The ResNet-50 model has over 1 billion parameters. | machine_learning | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B |
Given two Boolean random variables, A and B, where P(A) = 1/2, P(B) = 1/3, and P(A | ¬B) = 1/4, what is P(A | B)? | machine_learning | [
"1/6",
"1/4",
"3/4",
"1"
] | 3D |
For Kernel Regression, which one of these structural assumptions is the one that most affects the trade-off between underfitting and overfitting: | machine_learning | [
"Whether kernel function is Gaussian versus triangular versus box-shaped",
"Whether we use Euclidian versus L1 versus L∞ metrics",
"The kernel width",
"The maximum height of the kernel function"
] | 2C |
Consider the Bayesian network given below. How many independent parameters are needed for this Bayesian Network H -> U <- P <- W? | machine_learning | [
"2",
"4",
"8",
"16"
] | 2C |
What would you do in PCA to get the same projection as SVD? | machine_learning | [
"Transform data to zero mean",
"Transform data to zero median",
"Not possible",
"None of these"
] | 0A |
When doing least-squares regression with regularisation (assuming that the optimisation can be done exactly), increasing the value of the regularisation parameter λ the testing error. | machine_learning | [
"will never decrease the training error.",
"will never increase the training error.",
"will never decrease the testing error.",
"will never increase"
] | 0A |
Which of the following best describes what discriminative approaches try to model? (w are the parameters in the model) | machine_learning | [
"p(y|x, w)",
"p(y, x)",
"p(w|x, w)",
"None of the above"
] | 0A |
Which of the following tasks can be best solved using Clustering. | machine_learning | [
"Predicting the amount of rainfall based on various cues",
"Detecting fraudulent credit card transactions",
"Training a robot to solve a maze",
"All of the above"
] | 1B |
After applying a regularization penalty in linear regression, you find that some of the coefficients of w are zeroed out. Which of the following penalties might have been used? | machine_learning | [
"L0 norm",
"L1 norm",
"L2 norm",
"either (a) or (b)"
] | 3D |
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