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Recent questions tagged artificial-intelligence
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GATE DS&AI 2024 | Question: 13
Let $h_{1}$ and $h_{2}$ be two admissible heuristics used in $A^{*}$ search. Which ONE of the following expressions is always an admissible heuristic? $h_{1}+h_{2}$ $h_{1} \times h_{2}$ $h_{1} / h_{2},\left(h_{2} \neq 0\right)$ $\left|h_{1}-h_{2}\right|$
Let $h_{1}$ and $h_{2}$ be two admissible heuristics used in $A^{*}$ search.Which ONE of the following expressions is always an admissible heuristic?$h_...
Arjun
792
views
Arjun
asked
Feb 16
Artificial Intelligence
gate-ds-ai-2024
artificial-intelligence
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2
Memory Based GATE DA 2024 | Question: 32
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible:\(\frac{h_1}{h_2}\) \(\left(h_2 0\right)\)...
GO Classes
155
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GO Classes
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Feb 4
Artificial Intelligence
gate2024-da-memory-based
goclasses
artificial-intelligence
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Memory Based GATE DA 2024 | Question: 50
You are provided with three images, each depicting a different face of a six-sided dice. Based on these images, determine the correct option.
You are provided with three images, each depicting a different face of a six-sided dice. Based on these images, determine the correct option.
GO Classes
116
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GO Classes
asked
Feb 4
Artificial Intelligence
gate2024-da-memory-based
goclasses
artificial-intelligence
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2
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2
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Classification | Neural Network | DA
Suppose we want to classify movie review text as (1) either positive or negative sentiment, and (2) either action, comedy, or romance movie genre. To perform these two related classification tasks, we use a neural network that shares ... Adding more layers to the neural network. Splitting the model into two with more overall parameters. Reduce the traning Data
Suppose we want to classify movie review text as (1) either positive or negative sentiment, and (2) either action, comedy, or romance movie genre. To perform these two re...
rajveer43
284
views
rajveer43
asked
Jan 29
Machine Learning
machine-learning
artificial-intelligence
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1
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1
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ML | NN | DA Practice
Suppose that you are training a neural network for classification, but you notice that the training loss is much lower than the validation loss. Which of the following can be used to address the issue (select all that apply)? Use a network with fewer layers Decrease dropout probability √ Increase $L2$ regularization weight Increase the size of each hidden layer
Suppose that you are training a neural network for classification, but you notice that the training loss is much lower than the validation loss. Which of the following ca...
rajveer43
143
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rajveer43
asked
Jan 29
Machine Learning
machine-learning
artificial-intelligence
probability
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1
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1
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ML | DA | Classification
You encounter a classification task, and after training your network on 20 samples, the training converges, but the training loss is remarkably high. You decide to train the same network on 10,000 examples to address this issue. Is your approach to fixing ... of the model. D) No, a better approach would be to keep the same model architecture and increase the learning rate.
You encounter a classification task, and after training your network on 20 samples, the training converges, but the training loss is remarkably high. You decide to train ...
rajveer43
160
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rajveer43
asked
Jan 27
Machine Learning
machine-learning
artificial-intelligence
probability
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1
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Neural Network | ML | DA Practice
You have a single hidden-layer neural network for a binary classification task. The input is \(X \in \mathbb{R}^{n \times m}\), output \(\hat{y} \in \mathbb{R}^{1 \times m}\), and true label \(y \in \mathbb{R}^{1 \times m}\). The forward propagation equations are: ... $\frac{\partial J}{\partial W^{[1]}} = (\hat{y} - y) \cdot \sigma'(z^{[1]}) \cdot X^T$
You have a single hidden-layer neural network for a binary classification task. The input is \(X \in \mathbb{R}^{n \times m}\), output \(\hat{y} \in \mathbb{R}^{1 \times ...
rajveer43
153
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rajveer43
asked
Jan 27
Machine Learning
machine-learning
artificial-intelligence
statistics
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1
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ML | DA Practice Questions
What is Error Analysis? (i) The process of analyzing the performance of a model through metrics such as precision, recall or F1-score. (ii) The process of scanning mis-classified examples to identify weaknesses of a model. (iii) The process ... to reduce the loss function during training. (iv) The process of identifying which parts of your model contributed to the error.
What is Error Analysis?(i) The process of analyzing the performance of a model through metrics such as precision, recall or F1-score.(ii) The process of scanning mis-clas...
rajveer43
160
views
rajveer43
asked
Jan 27
Machine Learning
machine-learning
artificial-intelligence
statistics
probability
linear-algebra
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AI Sample Question for DS-AI
Imagine you are guiding a robot through a grid-based maze using the A* algorithm. The robot is currently at node A (start) and wants to reach node B (goal). The heuristic function $h(n)$ is the Euclidean distance between a node and the goal. The ... algorithm explore next based on the A* calculation? A) Node C B) Node D C) Node E D) Not enough information to decide
Imagine you are guiding a robot through a grid-based maze using the A* algorithm. The robot is currently at node A (start) and wants to reach node B (goal). The heuristi...
rajveer43
371
views
rajveer43
asked
Jan 16
Artificial Intelligence
artificial-intelligence
machine-learning
probability
statistics
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1
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2
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AIMT-2 GATE DA 2024| ML Question for GATE | Wanshington Uni | Aug-23 Mid term test
Suppose we are performing leave-one-out (LOO) validation and $10$-fold cross validation on a dataset of size $100, 000$ to pick between $4$ different values of a single hyperparameter. How many times greater is the number of models that need to be trained for LOO validation versus $10$-fold cross validation? Answer:
Suppose we are performing leave-one-out (LOO) validation and $10$-fold cross validation on a dataset of size $100, 000$ to pick between $4$ different values of a single h...
rajveer43
370
views
rajveer43
asked
Jan 16
Machine Learning
machine-learning
artificial-intelligence
probability
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0
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1
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UPENN | DS-AI Sample | Decision Tree
When choosing one feature from \(X_1, \ldots, X_n\) while building a Decision Tree, which of the following criteria is the most appropriate to maximize? (Here, \(H()\) means entropy, and \(P()\) means probability) (a) \(P(Y | X_j)\) (b) \(P(Y) - P(Y | X_j)\) (c) \(H(Y) - H(Y | X_j)\) (d) \(H(Y | X_j)\) (e) \(H(Y) - P(Y)\)
When choosing one feature from \(X_1, \ldots, X_n\) while building a Decision Tree, which of the following criteria is the most appropriate to maximize? (Here, \(H()\) me...
rajveer43
215
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rajveer43
asked
Jan 16
Artificial Intelligence
artificial-intelligence
machine-learning
statistics
probability
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0
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UPENN | ML | DECISION TREE
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. X Y Red True Green False Brown False Brown False (a) 1/2 (b) 1 (c) 3/2 (d) 2 (e) none of the above
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. XYRedTrueGreenFalseBrownFalseBrownF...
rajveer43
212
views
rajveer43
asked
Jan 16
Artificial Intelligence
artificial-intelligence
statistics
machine-learning
binary-tree
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1
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UPENN | ML Questions for GATE DA
In fitting some data using radial basis functions with kernel width $σ$, we compute training error of $345$ and a testing error of $390$. (a) increasing $σ$ will most likely reduce test set error (b) decreasing $σ$ will most likely reduce test set error (C) not enough information is provided to determine how $σ$ should be changed
In fitting some data using radial basis functions with kernel width $σ$, we compute training error of $345$ and a testing error of $390$.(a) increasing $σ$ will most li...
rajveer43
248
views
rajveer43
asked
Jan 15
Artificial Intelligence
machine-learning
statistics
artificial-intelligence
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DA Practice | UPENN | ML | Naive Bais
Suppose you have a three-class problem where class label \( y \in \{0, 1, 2\} \), and each training example \( \mathbf{X} \) has 3 binary attributes \( X_1, X_2, X_3 \in \{0, 1\} \). How many parameters do you need to know to classify an example using the Naive Bayes classifier? (a) 5 b) 9 (c) 11 (d) 13 (e) 23
Suppose you have a three-class problem where class label \( y \in \{0, 1, 2\} \), and each training example \( \mathbf{X} \) has 3 binary attributes \( X_1, X_2, X_3 \in ...
rajveer43
389
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rajveer43
asked
Jan 14
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
probability
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0
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2
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UPENN | ML | Cross validation
Suppose you have picked the parameter \( \theta \) for a model using 10-fold cross-validation. The best way to pick a final model to use and estimate its error is to (a) pick any of the 10 models you built for your model; use its error estimate on ... a new model on the full data set, using the \( \theta \) you found; use the average CV error as its error estimate
Suppose you have picked the parameter \( \theta \) for a model using 10-fold cross-validation. The best way to pick a final model to use and estimate its error is to(a) p...
rajveer43
250
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rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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1
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Decision Tree | Sample Question
$True$ or $False?$ If decision trees such as the ones we built in class are allowed to have decision nodes based on questions that can have many possible answers (e.g. “What country are you from) in addition to binary questions, they will in general tend to add the multiple answer questions to the tree before adding the binary questions
$True$ or $False?$ If decision trees such as the ones we built in class are allowed to have decision nodes based on questions that can have many possible answers (e.g. �...
rajveer43
223
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rajveer43
asked
Jan 13
Artificial Intelligence
algorithms
artificial-intelligence
machine-learning
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1
answer
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UPENN | ML | Cross Validation
P1: In the limit of infinite training and test data, consistent estimators always give at least as low a test error as biased estimators. P2: Leave-one out cross validation (LOOCV) generally gives less accurate estimates of true test error than 10-fold ... following Statements is/are correct? Only P1 is True Only P2 is True P1 is True and P2 is False Both are False
P1: In the limit of infinite training and test data, consistent estimators always give at least as low a test error as biased estimators. P2: Leave-one out cross validati...
rajveer43
190
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rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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1
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UPENN | ML | DA Practice | Regularization
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? (a) L0 norm (b) L1 norm (c) L2 norm (d) either (A) or (B) (e) any of the above
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 be...
rajveer43
254
views
rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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0
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0
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UPENN | ML | DA Practice
Using the same data as above \( \mathbf{X} = [-3, 5, 4] \) and \( \mathbf{Y} = [-10, 20, 20] \), assuming a ridge penalty \( \lambda = 50 \), what ratio versus the MLE estimate \( \hat{\mathbf{w}}_{\text{MLE}} \) do you think the ridge regression \( L_2 \) estimate \( \hat{\mathbf{w}}_{\text{ridge}} \) will be? (a)] 2 b)] 1 (c)] 0.666 (d)] 0.5
Using the same data as above \( \mathbf{X} = [-3, 5, 4] \) and \( \mathbf{Y} = [-10, 20, 20] \), assuming a ridge penalty \( \lambda = 50 \), what ratio versus the MLE es...
rajveer43
124
views
rajveer43
asked
Jan 13
Artificial Intelligence
artificial-intelligence
machine-learning
statistics
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0
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1
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20
UPENN | ML | DA Practice
Consider the statements: $P1:$ It is generally more important to use consistent estimators when one has smaller numbers of training examples. $P2:$ It is generally more important to used unbiased estimators when one has smaller numbers of training examples. Which of the following statement( ... $P1$ and $P2$ are true (C) Only $P2$ is True (D) Both $P1$ and $P2$ are False
Consider the statements:$P1:$ It is generally more important to use consistent estimators when one has smaller numbers of training examples.$P2:$ It is generally more imp...
rajveer43
128
views
rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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0
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1
answer
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DA Practice | UPENN | ML | Bias-Variance Trade Off | Regularization
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + k \|\mathbf{w}\|_p^p. \] What is the effect of increasing \( p ... , decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + k \|\mathbf{w}\|_p^p. \] What is the eff...
rajveer43
140
views
rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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0
votes
1
answer
22
UPENN | ML | DA Practice | Bias-Variance Trade-Off
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + \lambda \|\mathbf{w}\|_1. \] What is the effect of increasing \( \lambda \) ... bias, decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + \lambda \|\mathbf{w}\|_1. \] What is the...
rajveer43
113
views
rajveer43
asked
Jan 13
Artificial Intelligence
artificial-intelligence
machine-learning
statistics
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0
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1
answer
23
UPENN | Midterm | K Fold Validation | DA Practice |
Suppose we want to compute $10-Fold$ Cross-Validation error on $100$ training examples. We need to compute error $N1$ times, and the Cross-Validation error is the average of the errors. To compute each error, we need to build a model with data of size $N2$, and test the ... $N1 = 10, N2 = 100, N3 = 10$ (d) $N1 = 10, N2 = 100, N3 = 10$
Suppose we want to compute $10-Fold$ Cross-Validation error on $100$ training examples. We need to compute error $N1$ times, and the Cross-Validation error is the average...
rajveer43
111
views
rajveer43
asked
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
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0
votes
1
answer
24
Ai Questions | DS-AI Paper | GATE 2024
Given a tree with a branching factor of 3 and a depth of 4, calculate the maximum number of nodes expanded during a breadth-first search.
Given a tree with a branching factor of 3 and a depth of 4, calculate the maximum number of nodes expanded during a breadth-first search.
rajveer43
331
views
rajveer43
asked
Jan 1
Artificial Intelligence
discrete-mathematics
analytical-aptitude
quantitative-aptitude
artificial-intelligence
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0
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0
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25
GATE DS-AI questions | ML
Consider the feature transform z = [L0(x) L1(x) L2(x)]T with Legendre polynomials and the linear model h(x) = w T .z. For the regularized hypothesis with w = [−1 + 2 − 1] T , what is h(x) explicitly as a function of x? write solution for It.
Consider the feature transform z = [L0(x) L1(x) L2(x)]T with Legendre polynomials and the linear model h(x) = w T .z. For the regularized hypothesis with w = [−1 + 2 �...
rajveer43
330
views
rajveer43
asked
Dec 11, 2023
Artificial Intelligence
artificial-intelligence
machine-learning
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1
votes
1
answer
26
DRDO CSE 2022 Paper 2 | Question: 28 (a)
Provide the correct answer for the following: ________ is not the best evaluation metric for cancer prediction problem.
Provide the correct answer for the following:________ is not the best evaluation metric for cancer prediction problem.
admin
591
views
admin
asked
Dec 15, 2022
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
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1
votes
1
answer
27
DRDO CSE 2022 Paper 2 | Question: 29
$\max (0, x)$ and $\max (0.1 x, x)$ are _________ and ________ activation functions, respectively, which are non-linear in nature.
$\max (0, x)$ and $\max (0.1 x, x)$ are _________ and ________ activation functions, respectively, which are non-linear in nature.
admin
650
views
admin
asked
Dec 15, 2022
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
4-marks
fill-in-the-blanks
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2
votes
2
answers
28
DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?
What is the State $\mathrm{X}$ called for the following machine learning model?
admin
652
views
admin
asked
Dec 15, 2022
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
descriptive
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