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Recent questions and answers 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? $h_{1}+h_{2}$ $h_{1} \times h_{2}$ $h_{1} / h_{2},\left(h_{2} \neq 0\right)$ $\left|h_{1}-h_{2}\right|$
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_...
NarutoUzumaki
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NarutoUzumaki
answered
Feb 16
Artificial Intelligence
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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: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible:\(\frac{h_1}{h_2}\) \(\left(h_2 0\right)\)...
GO Classes
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GO Classes
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Feb 4
Artificial Intelligence
gate2024-da-memory-based
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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.
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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GO Classes
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Feb 4
Artificial Intelligence
gate2024-da-memory-based
goclasses
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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
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) p...
squirrel69
304
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squirrel69
answered
Jan 31
Artificial Intelligence
machine-learning
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What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
157
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Ameya Kulkarni
asked
Jan 30
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DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?
What is the State $\mathrm{X}$ called for the following machine learning model?
kaptaan_11
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kaptaan_11
answered
Jan 27
Artificial Intelligence
drdocse-2022-paper2
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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
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 ...
ruchit816
516
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ruchit816
answered
Jan 23
Artificial Intelligence
machine-learning
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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
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 li...
ruchit816
294
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ruchit816
answered
Jan 23
Artificial Intelligence
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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
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 be...
ruchit816
301
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ruchit816
answered
Jan 21
Artificial Intelligence
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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
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 heuristi...
rajveer43
416
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rajveer43
answered
Jan 16
Artificial Intelligence
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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) \(H(Y) - P(Y)\)
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, \(H()\) me...
rajveer43
276
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rajveer43
answered
Jan 16
Artificial Intelligence
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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
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. XYRedTrueGreenFalseBrownFalseBrownF...
rajveer43
303
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rajveer43
answered
Jan 16
Artificial Intelligence
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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
$True$ 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. �...
prasantkr.singh
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prasantkr.singh
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Jan 15
Artificial Intelligence
algorithms
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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 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
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 validati...
rajveer43
227
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rajveer43
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Jan 13
Artificial Intelligence
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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
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 es...
rajveer43
150
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rajveer43
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Jan 13
Artificial Intelligence
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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
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 imp...
rajveer43
164
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rajveer43
answered
Jan 13
Artificial Intelligence
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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
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 eff...
rajveer43
197
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rajveer43
answered
Jan 13
Artificial Intelligence
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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
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...
rajveer43
138
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rajveer43
answered
Jan 13
Artificial Intelligence
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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) $N1 = 10, N2 = 100, N3 = 10$
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...
rajveer43
151
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rajveer43
answered
Jan 13
Artificial Intelligence
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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
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...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
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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
Which of the following is NOT true in problem solving in artificial intelligence?Implements heuristic search techniqueSolution steps are not explicitKnowledge is imprecis...
rajveer43
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rajveer43
answered
Jan 3
Artificial Intelligence
ugcnetcse-oct2020-paper2
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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, $g=1$ $g=0$ $h'=0$ $h'=1$
$A^*$ 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 ...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2012-paper3
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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
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 outputweights...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2012-paper3
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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
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 source...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2012-paper2
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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
Forward chaining systems are ____ where as backward chaining systems are ____Data driven, Data drivenGoal driven, Data drivenData driven, Goal drivenGoal driven, Goal dri...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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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
Reasoning strategies used in expert systems includeForward chaining, backward chaining and problem reductionForward chaining, backward chaining and boundary mutationForwa...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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UGC NET CSE | December 2015 | Part 3 | Question: 46
Language model used in LISP is Functional programming Logic programming Object oriented programming All of the above
Language model used in LISP isFunctional programmingLogic programmingObject oriented programmingAll of the above
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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28
Artificial Intelligence Heuristic problem Confusion
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
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UGC NET CSE | July 2018 | Part 2 | Question: 73
In heuristic search algorithms in Artificial Intelligence (AI), if a collection of admissible heuristics $h_1 \dots h_m$ is available for a problem and none of them dominates any of the others, which should we choose? $h(n)=max\{h_1(n), \dots , h_m(n)\}$ ... $h(n)=avg\{h_1(n), \dots , h_m(n)\}$ $h(n)=sum\{h_1(n), \dots , h_m(n)\}$
In heuristic search algorithms in Artificial Intelligence (AI), if a collection of admissible heuristics $h_1 \dots h_m$ is available for a problem and none of them domin...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
artificial-intelligence
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UGC NET CSE | July 2018 | Part 2 | Question: 78
Consider the following two sentences: The planning graph data structure can be used to give a better heuristic for a planning problem Dropping negative effects from every action schema in a planning problem results in a relaxed problem Which of the ... b are true Sentence a is true but sentence b is false Sentence a is false but sentence b is true
Consider the following two sentences:The planning graph data structure can be used to give a better heuristic for a planning problemDropping negative effects from every a...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
planning
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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
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 onl...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2019-paper2
artificial-intelligence
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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 $x_2=2.2?$ $-2.65$ $-2.30$ $0$ $1$
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$
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2016-paper3
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UGC NET CSE | July 2018 | Part 2 | Question: 74
Consider following sentences regarding $A^*$, an informed search strategy in Artificial Intelligence (AI). $A^*$ expands all nodes with $f(n)<C^*$ $A^*$ expands no nodes with $f(n) \geq C^*$ Pruning is integral to $A^*$ ... Both statements a and statement c are true Both statements b and statement c are true All the statements a, b and c are true
Consider following sentences regarding $A^*$, an informed search strategy in Artificial Intelligence (AI).$A^*$ expands all nodes with $f(n)<C^*$$A^*$ expands no nodes wi...
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
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34
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.
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 thi...
rajveer43
445
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rajveer43
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Jan 3
Artificial Intelligence
machine-learning
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35
Machine Learning Self-doubt
Please Solve this question with full explanation.
Please Solve this question with full explanation.
rajveer43
283
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rajveer43
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Jan 3
Artificial Intelligence
machine-learning
self-doubt
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36
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.
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.
C.Aravind REDDY
386
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C.Aravind REDDY
answered
Jan 2
Artificial Intelligence
discrete-mathematics
analytical-aptitude
quantitative-aptitude
artificial-intelligence
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37
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.
Provide the correct answer for the following:________ is not the best evaluation metric for cancer prediction problem.
Tejas07
625
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Tejas07
answered
Dec 29, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
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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.
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 �...
rajveer43
366
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rajveer43
asked
Dec 11, 2023
Artificial Intelligence
artificial-intelligence
machine-learning
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39
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___________.
Provide the correct answer for the following:The phenomena in which training error of the model decreases but test error increases is called___________.
Lakshay Kakkar
528
views
Lakshay Kakkar
answered
Dec 3, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
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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 $y$ ...
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 $0,$ wh...
Kazuha
410
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Kazuha
answered
Oct 3, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
activation-function
5-marks
descriptive
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