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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} ... 1} / h_{2},\left(h_{2} \neq 0\right)$\left|h_{1}-h_{2}\right|$
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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$12$
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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 ... {h}_2\) \\\(\left| h_1 - h_2 \right|\) \\\(h_1 + h_2\)
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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.
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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 sinkBackward from sink to sourceForward from source to hidden nodesBackward from sink to hidden nodes
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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 ... the \( \theta \) you found; use the average CV error as its error estimate
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Required Artificial Intelligence resources for UGC NET
HiI 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.
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UGC NET CSE | June 2019 | Part 2 | Question: 97
Consider the following:EvolutionSelectionReproductionMutationWhich of the following are found in genetic algorithms?b, c and d onlyb and d onlya, b, c and da, b and d only
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DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?
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ISRO2011-2
Which of the following is an unsupervised neural network?RBSHopfieldBack propagationKohonen
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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, ... an example using the Naive Bayes classifier?(a) 5b) 9(c) 11(d) 13(e) 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 ... error(C) not enough information is provided to determine how $σ$ should be changed
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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) ... (c) L2 norm(d) either (A) or (B)(e) any of the above
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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 $X$. XYRedTrueGreenFalseBrownFalseBrownFalse (a) 1/2(b) 1(c) 3/2(d) 2(e) none of the above
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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.
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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). ... A* calculation? A) Node CB) Node DC) Node ED) Not enough information to decide
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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 \(P( ... X_j)\)(d) \(H(Y | X_j)\)(e) \(H(Y) - P(Y)\)
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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 ... tend to add the multiple answer questions to the tree before adding the binary questions
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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 ... ?Only P1 is TrueOnly P2 is TrueP1 is True and P2 is FalseBoth are False
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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 ... \mathbf{w}}_{\text{ridge}} \) will be?(a)] 2b)] 1(c)] 0.666(d)] 0.5
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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 ... C) Only $P2$ is True(D) Both $P1$ and $P2$ are False
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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 \|\ ... bias, increases variance(d)] Decreases bias, decreases variance(e)] Not enough information to tell
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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 \ ... , increases variance(d)] Decreases bias, decreases variance(e)] Not enough information to tell
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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. ... $N1 = 10, N2 = 100, N3 = 10$
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ISRO2018-75
ln neural network, the network capacity is defined as:The traffic (tarry capacity of the networkThe total number of nodes in the networkThe number of patterns that can be stored and recalled in a networkNone of the above
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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 techniqueSolution steps are not explicitKnowledge is impreciseIt works on or implements repetition mechanism
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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 ... $g=0$h'=0$h'=1$
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UGC NET CSE | June 2012 | Part 3 | Question: 2
In Delta Rule for error minimizationweights are adjusted w.r.to change in the outputweights are adjusted w.r.to difference between desired output and actual ... are adjusted w.r.to difference between output and outputnone of the above
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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 drivenGoal driven, Data drivenData driven, Goal drivenGoal driven, Goal driven
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