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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
asked
in
Artificial Intelligence
Feb 16
by
Arjun
788
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gate-ds-ai-2024
artificial-intelligence
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
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in
Artificial Intelligence
Feb 4
by
GO Classes
161
views
gate2024-da-memory-based
goclasses
artificial-intelligence
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
by
GO Classes
122
views
gate2024-da-memory-based
goclasses
artificial-intelligence
0
votes
0
answers
4
What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
asked
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
asked
in
Artificial Intelligence
Jan 16
by
rajveer43
378
views
artificial-intelligence
machine-learning
probability
statistics
0
votes
1
answer
6
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
217
views
artificial-intelligence
statistics
machine-learning
binary-tree
0
votes
1
answer
7
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
259
views
machine-learning
statistics
artificial-intelligence
0
votes
1
answer
8
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
392
views
machine-learning
artificial-intelligence
statistics
probability
0
votes
2
answers
9
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
257
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
10
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
11
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
12
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
13
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
128
views
artificial-intelligence
machine-learning
statistics
0
votes
1
answer
14
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
15
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
155
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
16
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
122
views
artificial-intelligence
machine-learning
statistics
0
votes
1
answer
17
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
0
answers
18
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
views
artificial-intelligence
machine-learning
0
votes
1
answer
19
Machine Learning Self-doubt
Please Solve this question with full explanation.
gateexplore
asked
in
Artificial Intelligence
Nov 30, 2023
by
gateexplore
254
views
machine-learning
self-doubt
1
vote
1
answer
20
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
603
views
drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
1
vote
1
answer
21
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
drdocse-2022-paper2
artificial-intelligence
4-marks
fill-in-the-blanks
2
votes
2
answers
22
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
676
views
drdocse-2022-paper2
artificial-intelligence
2-marks
descriptive
1
vote
1
answer
23
DRDO CSE 2022 Paper 2 | Question: 32
A perceptron consists of weights $\left[w_{1}, w_{2}, w_{3}, w_{4}\right]=[0.5,2,1,-3]$. The activation function is provided as $y=f(z)=1$ if $z \geq 2$ otherwise $0,$ where $z= \sum(w . d)$. What is the output $y$ ...
admin
asked
in
Artificial Intelligence
Dec 15, 2022
by
admin
392
views
drdocse-2022-paper2
artificial-intelligence
activation-function
5-marks
descriptive
1
vote
1
answer
24
DRDO CSE 2022 Paper 2 | Question: 28 (b)
Provide the correct answer for the following: The phenomena in which training error of the model decreases but test error increases is called___________.
admin
asked
in
Artificial Intelligence
Dec 15, 2022
by
admin
522
views
drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
1
vote
1
answer
25
Machine Learning
You are a designing a machine learning model for a binary classification problem. The model has three features: f1, f2, f3. Derive the objective and loss function for this problem.
rayhanrjt
asked
in
Artificial Intelligence
Nov 25, 2022
by
rayhanrjt
435
views
machine-learning
0
votes
4
answers
26
UGC NET CSE | June 2016 | Part 3 | Question: 66
A perceptron has input weights $W_1=-3.9$ and $W_2=1.1$ with threshold value $T=0.3.$ What output does it give for the input $x_1=1.3$ and $x_2=2.2?$ $-2.65$ $-2.30$ $0$ $1$
soujanyareddy13
asked
in
Artificial Intelligence
May 10, 2021
by
soujanyareddy13
1.3k
views
ugcnetcse-june2016-paper3
0
votes
1
answer
27
UGC NET CSE | June 2016 | Part 3 | Question: 75
A software program that infers and manipulates existing knowledge in order to generate new knowledge is known as: Data dictionary Reference mechanism Inference engine Control strategy
soujanyareddy13
asked
in
Artificial Intelligence
May 10, 2021
by
soujanyareddy13
831
views
ugcnetcse-june2016-paper3
0
votes
2
answers
28
UGC NET CSE | October 2020 | Part 2 | Question: 36
Which of the following is NOT true in problem solving in artificial intelligence? Implements heuristic search technique Solution steps are not explicit Knowledge is imprecise It works on or implements repetition mechanism
go_editor
asked
in
Artificial Intelligence
Nov 20, 2020
by
go_editor
1.4k
views
ugcnetcse-oct2020-paper2
non-gate
artificial-intelligence
0
votes
3
answers
29
UGC NET CSE | January 2017 | Part 3 | Question: 55
Consider following two rules $\text{R1}$ and $\text{R2}$ in logical reasoning in Artificial Intelligence (AI): $\text{R1}:$ From $\alpha \supset \beta \; \frac{\text{and}\; \alpha}{\text{Inter} \; \beta }$ is known as Modulus Tollens (MT) ... is correct. Both $\text{R1}$ and $\text{R2}$ are correct. Neither $\text{R1}$ nor $\text{R2}$ is correct.
go_editor
asked
in
Artificial Intelligence
Mar 24, 2020
by
go_editor
1.5k
views
ugcnetcse-jan2017-paper3
non-gate
artificial-intelligence
2
votes
1
answer
30
UGC NET CSE | June 2019 | Part 2 | Question: 91
Consider the game tree given below: Here $\bigcirc$ and $\Box$ represents MIN and MAX nodes respectively. The value of the root node of the game tree is $4$ $7$ $11$ $12$
Arjun
asked
in
Artificial Intelligence
Jul 2, 2019
by
Arjun
3.1k
views
ugcnetcse-june2019-paper2
artificial-intelligence
minimax-procedure
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