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

makhdoom ghaya edited in Artificial Intelligence Mar 17

by makhdoom ghaya

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

Lakshman Bhaiya recategorized in Artificial Intelligence Feb 4

by Lakshman Bhaiya

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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.

Lakshman Bhaiya recategorized in Artificial Intelligence Feb 4

by Lakshman Bhaiya

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UGC NET CSE | December 2012 | Part 2 | Question: 46
Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes. Forward from source to sink Backward from sink to source Forward from source to hidden nodes Backward from sink to hidden nodes

aryan1113 commented in Artificial Intelligence Jan 31

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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

squirrel69 answered in Artificial Intelligence Jan 31

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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

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Required Artificial Intelligence resources for UGC NET
Hi I need some useful resources of AI for upcoming UGC NET exam. Currently, I am reading from Rich and Knight, Is it enough? Please let me know about any good quality books or video lectures.

makhdoom ghaya retagged in Artificial Intelligence Jan 28

by makhdoom ghaya

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UGC NET CSE | June 2019 | Part 2 | Question: 97
Consider the following: Evolution Selection Reproduction Mutation Which of the following are found in genetic algorithms? b, c and d only b and d only a, b, c and d a, b and d only

makhdoom ghaya edited in Artificial Intelligence Jan 28

by makhdoom ghaya

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DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?

kaptaan_11 answered in Artificial Intelligence Jan 27

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ISRO2011-2
Which of the following is an unsupervised neural network? RBS Hopfield Back propagation Kohonen

makhdoom ghaya edited in Artificial Intelligence Jan 24

by makhdoom ghaya

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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

rajveer43 commented in Artificial Intelligence Jan 23

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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

rajveer43 commented in Artificial Intelligence Jan 23

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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

rajveer43 commented in Artificial Intelligence Jan 21

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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

ruchit816 commented in Artificial Intelligence Jan 21

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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 answer selected in Artificial Intelligence Jan 16

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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

rajveer43 answered in Artificial Intelligence Jan 16

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

rajveer43 answer selected in Artificial Intelligence Jan 16

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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

prasantkr.singh answered in Artificial Intelligence Jan 15

by prasantkr.singh

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19

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 answered in Artificial Intelligence Jan 13

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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

rajveer43 asked in Artificial Intelligence Jan 13

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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 answer edited in Artificial Intelligence Jan 13

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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

rajveer43 answered in Artificial Intelligence Jan 13

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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 answered in Artificial Intelligence Jan 13

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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 answered in Artificial Intelligence Jan 13

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ISRO2018-75
ln neural network, the network capacity is defined as: The traffic (tarry capacity of the network The total number of nodes in the network The number of patterns that can be stored and recalled in a network None of the above

rajveer43 answered in Artificial Intelligence Jan 3

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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

rajveer43 answered in Artificial Intelligence Jan 3

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UGC NET CSE | June 2012 | Part 3 | Question: 21
$A^*$ algorithm uses $f'=g+h'$ to estimate the cost of getting from the initial state to the goal state, where $g$ is a measure of cost getting from initial state to the current node and the function $h'$ is an estimate of the cost of getting from the ... . To find a path involving the fewest number of steps, we should test, $g=1$ $g=0$ $h'=0$ $h'=1$

rajveer43 answered in Artificial Intelligence Jan 3

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UGC NET CSE | June 2012 | Part 3 | Question: 2
In Delta Rule for error minimization weights are adjusted w.r.to change in the output weights are adjusted w.r.to difference between desired output and actual output weights are adjusted w.r.to difference between output and output none of the above

rajveer43 answered in Artificial Intelligence Jan 3

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UGC NET CSE | December 2015 | Part 3 | Question: 8
Forward chaining systems are ____ where as backward chaining systems are ____ Data driven, Data driven Goal driven, Data driven Data driven, Goal driven Goal driven, Goal driven

rajveer43 answered in Artificial Intelligence Jan 3

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UGC NET CSE | December 2015 | Part 3 | Question: 45
Reasoning strategies used in expert systems include Forward chaining, backward chaining and problem reduction Forward chaining, backward chaining and boundary mutation Forward chaining, backward chaining and back propagation Forward chaining, problem reduction and boundary mutation

rajveer43 answered in Artificial Intelligence Jan 3

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