Dark Mode

Recent questions tagged artificial-intelligence

0 votes

1 answer

1

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? $h_{1}+h_{2}$ $h_{1} \times h_{2}$ $h_{1} / h_{2},\left(h_{2} \neq 0\right)$ $\left|h_{1}-h_{2}\right|$

Arjun asked in Artificial Intelligence Feb 16

by Arjun

787 views

0 votes

0 answers

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: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)

GO Classes asked in Artificial Intelligence Feb 4

161 views

0 votes

0 answers

3

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.

GO Classes asked in Artificial Intelligence Feb 4

121 views

2 votes

2 answers

4

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

rajveer43 asked in Machine Learning Jan 29

291 views

1 vote

1 answer

5

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

rajveer43 asked in Machine Learning Jan 29

150 views

1 vote

1 answer

6

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.

rajveer43 asked in Machine Learning Jan 27

164 views

0 votes

1 answer

7

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: ... $\frac{\partial J}{\partial W^{[1]}} = (\hat{y} - y) \cdot \sigma'(z^{[1]}) \cdot X^T$

rajveer43 asked in Machine Learning Jan 27

157 views

0 votes

1 answer

8

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.

rajveer43 asked in Machine Learning Jan 27

164 views

0 votes

1 answer

9

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

rajveer43 asked in Artificial Intelligence Jan 16

377 views

1 vote

2 answers

10

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:

rajveer43 asked in Machine Learning Jan 16

378 views

0 votes

1 answer

11

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) \(H(Y) - P(Y)\)

rajveer43 asked in Artificial Intelligence Jan 16

221 views

0 votes

1 answer

12

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

rajveer43 asked in Artificial Intelligence Jan 16

216 views

0 votes

1 answer

13

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

rajveer43 asked in Artificial Intelligence Jan 15

258 views

0 votes

1 answer

14

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

rajveer43 asked in Artificial Intelligence Jan 14

391 views

0 votes

2 answers

15

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

rajveer43 asked in Artificial Intelligence Jan 13

256 views

0 votes

1 answer

16

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

rajveer43 asked in Artificial Intelligence Jan 13

219 views

0 votes

1 answer

17

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

rajveer43 asked in Artificial Intelligence Jan 13

194 views

0 votes

1 answer

18

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

rajveer43 asked in Artificial Intelligence Jan 13

264 views

0 votes

0 answers

19

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

rajveer43 asked in Artificial Intelligence Jan 13

126 views

0 votes

1 answer

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

rajveer43 asked in Artificial Intelligence Jan 13

133 views

0 votes

1 answer

21

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

rajveer43 asked in Artificial Intelligence Jan 13

152 views

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

rajveer43 asked in Artificial Intelligence Jan 13

121 views

0 votes

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) $N1 = 10, N2 = 100, N3 = 10$

rajveer43 asked in Artificial Intelligence Jan 13

120 views

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.

rajveer43 asked in Artificial Intelligence Jan 1

345 views

0 votes

0 answers

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.

rajveer43 asked in Artificial Intelligence Dec 11, 2023

332 views

1 vote

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.

admin asked in Artificial Intelligence Dec 15, 2022

by admin

602 views

1 vote

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.

admin asked in Artificial Intelligence Dec 15, 2022

by admin

658 views

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?

admin asked in Artificial Intelligence Dec 15, 2022

by admin

672 views

Page:

1
2
3
next »

Recent questions tagged artificial-intelligence