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Recent questions in 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? $h_{1}+h_{2}$ $h_{1} \times h_{2}$ $h_{1} / h_{2},\left(h_{2} \neq 0\right)$ $\left|h_{1}-h_{2}\right|$
Arjun
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Artificial Intelligence
Feb 16
by
Arjun
787
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gate-ds-ai-2024
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0
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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: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)
GO Classes
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Feb 4
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GO Classes
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gate2024-da-memory-based
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artificial-intelligence
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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
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Artificial Intelligence
Feb 4
by
GO Classes
122
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gate2024-da-memory-based
goclasses
artificial-intelligence
0
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4
What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
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in
Artificial Intelligence
Jan 30
by
Ameya Kulkarni
149
views
0
votes
1
answer
5
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
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in
Artificial Intelligence
Jan 16
by
rajveer43
378
views
artificial-intelligence
machine-learning
probability
statistics
0
votes
1
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6
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
by
rajveer43
221
views
artificial-intelligence
machine-learning
statistics
probability
0
votes
1
answer
7
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
by
rajveer43
216
views
artificial-intelligence
statistics
machine-learning
binary-tree
0
votes
1
answer
8
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
by
rajveer43
258
views
machine-learning
statistics
artificial-intelligence
0
votes
1
answer
9
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
by
rajveer43
391
views
machine-learning
artificial-intelligence
statistics
probability
0
votes
2
answers
10
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
by
rajveer43
256
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
11
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
by
rajveer43
219
views
algorithms
artificial-intelligence
machine-learning
0
votes
1
answer
12
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
by
rajveer43
194
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
13
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
by
rajveer43
265
views
machine-learning
artificial-intelligence
statistics
0
votes
0
answers
14
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
by
rajveer43
127
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artificial-intelligence
machine-learning
statistics
0
votes
1
answer
15
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
by
rajveer43
133
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
16
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
by
rajveer43
154
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machine-learning
artificial-intelligence
statistics
0
votes
1
answer
17
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
by
rajveer43
121
views
artificial-intelligence
machine-learning
statistics
0
votes
1
answer
18
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
by
rajveer43
121
views
machine-learning
artificial-intelligence
0
votes
1
answer
19
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
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in
Artificial Intelligence
Jan 1
by
rajveer43
345
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discrete-mathematics
analytical-aptitude
quantitative-aptitude
artificial-intelligence
0
votes
0
answers
20
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
by
rajveer43
332
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