question stringclasses 28
values | choices sequence | answer class label 4
classes | neg_prompt stringclasses 15
values |
|---|---|---|---|
_ refers to a model that can neither model the training data nor generalize to new data. | [
"good fitting",
"overfitting",
"underfitting",
"all of the above"
] | 2C | Question: _ doesn't refer to a model that can neither model the training data nor generalize to new data.
Answer: good fitting |
Statement 1| The back-propagation algorithm learns a globally optimal neural network with hidden layers. Statement 2| The VC dimension of a line should be at most 2, since I can find at least one case of 3 points that cannot be shattered by any line. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| Layer Normalization is used in the original ResNet paper, not Batch Normalization. Statement 2| DCGANs use self-attention to stabilize training. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
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 | [
"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 | Question: In building a linear regression model for a particular data set, you don't observe the coefficient of one of the features having a relatively high negative value. This suggests that
Answer: This feature does not have a strong effect on the model (should be ignored) |
Statement 1| As of 2020, some models attain greater than 98% accuracy on CIFAR-10. Statement 2| The original ResNets were not optimized with the Adam optimizer. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
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... | [
"0.0368",
"0.473",
"0.078",
"None of the above"
] | 2C | Question: Say the incidence of a disease D isn't 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 po... |
Which of the following sentence is FALSE regarding regression? | [
"It relates inputs to outputs.",
"It is used for prediction.",
"It may be used for interpretation.",
"It discovers causal relationships"
] | 3D | Question: Which of the following sentence isn't FALSE regarding regression?
Answer: It relates inputs to outputs. |
Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting? | [
"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 | Question: Don't suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?
Answer: Increase the amount of training data. |
Given a Neural Net with N input nodes, no hidden layers, one output node, with Entropy Loss and Sigmoid Activation Functions, which of the following algorithms (with the proper hyper-parameters and initialization) can be used to find the global optimum? | [
"Stochastic Gradient Descent",
"Mini-Batch Gradient Descent",
"Batch Gradient Descent",
"All of the above"
] | 3D | Question: Given a Neural Net with N input nodes, no hidden layers, one output node, with Entropy Loss and Sigmoid Activation Functions, which of the following algorithms (with the proper hyper-parameters and initialization) can't be used to find the global optimum?
Answer: All of the above |
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? | [
"3",
"4",
"7",
"15"
] | 3D | Question: Don't 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?
Answer: 4 |
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... | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| Highway networks were introduced after ResNets and eschew max pooling in favor of convolutions. Statement 2| DenseNets usually cost more memory than ResNets. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| The original ResNets and Transformers are feedforward neural networks. Statement 2| The original Transformers use self-attention, but the original ResNet does not. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| RELUs are not monotonic, but sigmoids are monotonic. Statement 2| Neural networks trained with gradient descent with high probability converge to the global optimum. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation? | [
"P(E, F), P(H), P(E|H), P(F|H)",
"P(E, F), P(H), P(E, F|H)",
"P(H), P(E|H), P(F|H)",
"P(E, F), P(E|H), P(F|H)"
] | 1B | Question: Don't suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation?
Answer: P(E, F), P(H), P(E|H), P(F|H) |
Statement 1| The Stanford Sentiment Treebank contained movie reviews, not book reviews. Statement 2| The Penn Treebank has been used for language modeling. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| Word2Vec parameters were not initialized using a Restricted Boltzman Machine. Statement 2| The tanh function is a nonlinear activation function. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
If your training loss increases with number of epochs, which of the following could be a possible issue with the learning process? | [
"Regularization is too low and model is overfitting",
"Regularization is too high and model is underfitting",
"Step size is too large",
"Step size is too small"
] | 2C | Question: If your training loss increases with number of epochs, which of the following couldn't be a possible issue with the learning process?
Answer: Step size is too small |
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... | [
"0.0495",
"0.078",
"0.635",
"0.97"
] | 2C | Question: Say the incidence of a disease D isn't 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 po... |
Statement 1| The values of the margins obtained by two different kernels K1(x, x0) and K2(x, x0) on the same training set do not tell us which classifier will perform better on the test set. Statement 2| The activation function of BERT is the GELU. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
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? | [
"Your decision trees are too shallow.",
"You need to increase the learning rate.",
"You are overfitting.",
"None of the above."
] | 0A | Question: You haven't 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?
Answer: None of the above. |
Statement 1| Industrial-scale neural networks are normally trained on CPUs, not GPUs. Statement 2| The ResNet-50 model has over 1 billion parameters. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| Maximizing the likelihood of logistic regression model yields multiple local optimums. Statement 2| No classifier can do better than a naive Bayes classifier if the distribution of the data is known. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Statement 1| CIFAR-10 classification performance for convolution neural networks can exceed 95%. Statement 2| Ensembles of neural networks do not improve classification accuracy since the representations they learn are highly correlated. | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
Which of the following is NOT supervised learning? | [
"PCA",
"Decision Tree",
"Linear Regression",
"Naive Bayesian"
] | 0A | Question: Which of the following is supervised learning?
Answer: Linear Regression |
Which one of the following is equal to P(A, B, C) given Boolean random variables A, B and C, and no independence or conditional independence assumptions between any of them? | [
"P(A | B) * P(B | C) * P(C | A)",
"P(C | A, B) * P(A) * P(B)",
"P(A, B | C) * P(C)",
"P(A | B, C) * P(B | A, C) * P(C | A, B)"
] | 2C | Question: Which one of the following isn't equal to P(A, B, C) given Boolean random variables A, B and C, and no independence or conditional independence assumptions between any of them?
Answer: P(C | A, B) * P(A) * P(B) |
Statement 1| When learning an HMM for a fixed set of observations, assume we do not know the true number of hidden states (which is often the case), we can always increase the training data likelihood by permitting more hidden states. Statement 2| Collaborative filtering is often a useful model for modeling users' movi... | [
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Question: Statement 1| The derivative of the sigmoid $\sigma(x)=(1+e^{-x})^{-1}$ with respect to $x$ isn't equal to $\text{Var}(B)$ where $B\sim \text{Bern}(\sigma(x))$ is a Bernoulli random variable. Statement 2| Setting the bias parameters in each layer of neural network to 0 changes the bias-variance trade-off such ... |
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... | [
"111021",
"110010",
"111110",
"110011"
] | 0A | Question: We aren't 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. Th... |
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