60 + Interview Questions in Learning in Artificial Intelligence Page 1 InterviewSolution

1.	Which method can’t be used for expressing relational knowledge?(a) Literal system(b) Variable-based system(c) Attribute-based system(d) None of the mentionedThis question was posed to me in semester exam.Query is from Inductive logic programming topic in division Learning of Artificial Intelligence
Answer» CORRECT option is (C) Attribute-based system For explanation: ILP methods can LEARN RELATIONAL knowledge that is not expressible in attribute-based system.

Discussion

2.	Which approach is used for refining a very general rule through ILP?(a) Top-down approach(b) Bottom-up approach(c) Both Top-down & Bottom-up approach(d) None of the mentionedThis question was posed to me in unit test.This intriguing question originated from Inductive logic programming in section Learning of Artificial Intelligence
Answer» CORRECT CHOICE is (a) Top-down approach Easiest EXPLANATION: NONE.

Discussion

3.	How many literals are available in top-down inductive learning methods?(a) 1(b) 2(c) 3(d) 4I got this question during an online exam.The question is from Inductive logic programming in section Learning of Artificial Intelligence
Answer» CORRECT answer is (C) 3 Easy explanation: The three LITERALS are available in top-down inductive learning methods are predicates, equality and INEQUALITY and arithmetic literals.

Discussion

4.	Which inverts a complete resolution strategy?(a) Inverse resolution(b) Resolution(c) Trilogy(d) None of the mentionedI have been asked this question in semester exam.This key question is from Inductive logic programming topic in section Learning of Artificial Intelligence
Answer» The CORRECT answer is (a) INVERSE resolution The EXPLANATION: Because it is a complete algorithm for LEARNING first-order theories.

Discussion

5.	What need to be satisfied in inductive logic programming?(a) Constraint(b) Entailment constraint(c) Both Constraint & Entailment constraint(d) None of the mentionedThe question was asked by my college professor while I was bunking the class.Query is from Inductive logic programming topic in division Learning of Artificial Intelligence
Answer» RIGHT answer is (b) ENTAILMENT CONSTRAINT Easiest explanation: The objective of an ILP is to come up with a set of sentences for the HYPOTHESIS such that the entailment constraint is SATISFIED.

Discussion

6.	Which produces hypotheses that are easy to read for humans?(a) ILP(b) Artificial intelligence(c) Propositional logic(d) First-order logicThe question was posed to me in a job interview.My query is from Inductive logic programming in chapter Learning of Artificial Intelligence
Answer» Right OPTION is (a) ILP Explanation: Because ILP can participate in the scientific CYCLE of experimentation, So that it can PRODUCE FLEXIBLE structure.

Discussion

7.	Which is an appropriate language for describing the relationships?(a) First-order logic(b) Propositional logic(c) ILP(d) None of the mentionedThis question was addressed to me during a job interview.Asked question is from Inductive logic programming topic in portion Learning of Artificial Intelligence
Answer» RIGHT OPTION is (a) First-order logic The EXPLANATION: NONE.

Discussion

8.	Which cannot be represented by a set of attributes?(a) Program(b) Three-dimensional configuration of a protein molecule(c) Agents(d) None of the mentionedThis question was posed to me in my homework.This key question is from Inductive logic programming in division Learning of Artificial Intelligence
Answer» Correct choice is (b) Three-dimensional configuration of a PROTEIN molecule Best explanation: Because the configuration INHERENTLY refers to RELATIONSHIPS between OBJECTS.

Discussion

9.	Which combines inductive methods with the power of first-order representations?(a) Inductive programming(b) Logic programming(c) Inductive logic programming(d) Lisp programmingI had been asked this question during an interview.The above asked question is from Inductive logic programming in chapter Learning of Artificial Intelligence
Answer» The correct answer is (c) INDUCTIVE logic programming The explanation is: Inductive logic programming(ILP) combines inductive methods with the POWER of first-order representations.

Discussion

10.	How many reasons are available for the popularity of ILP?(a) 1(b) 2(c) 3(d) 4This question was posed to me during an online interview.I would like to ask this question from Inductive logic programming in chapter Learning of Artificial Intelligence
Answer» The correct option is (c) 3 To elaborate: The three reasons available for the popularity of ILP are GENERAL knowledge, Complete ALGORITHM and HYPOTHESES.

Discussion

11.	End Nodes are represented by __________(a) Disks(b) Squares(c) Circles(d) TrianglesI have been asked this question during an internship interview.This question is from Decision Trees in division Learning of Artificial Intelligence
Answer» The CORRECT OPTION is (d) Triangles To explain I would say: NONE.

Discussion

12.	Chance Nodes are represented by __________(a) Disks(b) Squares(c) Circles(d) TrianglesThis question was posed to me in an online interview.Question is taken from Decision Trees topic in portion Learning of Artificial Intelligence
Answer» CORRECT OPTION is (C) Circles The EXPLANATION is: NONE.

Discussion

13.	Choose from the following that are Decision Tree nodes?(a) Decision Nodes(b) End Nodes(c) Chance Nodes(d) All of the mentionedI got this question in my homework.Query is from Decision Trees topic in section Learning of Artificial Intelligence
Answer» Right OPTION is (d) All of the mentioned For EXPLANATION I would say: NONE.

Discussion

14.	Decision Nodes are represented by ____________(a) Disks(b) Squares(c) Circles(d) TrianglesThis question was posed to me in homework.Query is from Decision Trees topic in section Learning of Artificial Intelligence
Answer» The CORRECT OPTION is (B) Squares To ELABORATE: NONE.

Discussion

15.	Decision Trees can be used for Classification Tasks.(a) True(b) FalseI have been asked this question in examination.This intriguing question comes from Decision Trees in chapter Learning of Artificial Intelligence
Answer» RIGHT ANSWER is (a) True Explanation: NONE.

Discussion

16.	What is Decision Tree?(a) Flow-Chart(b) Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label(c) Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label(d) None of the mentionedThis question was addressed to me in an online quiz.This is a very interesting question from Decision Trees topic in chapter Learning of Artificial Intelligence
Answer» The correct CHOICE is (C) Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents OUTCOME of test and each leaf node represents CLASS label The best I can explain: Refer the definition of Decision tree.

Discussion

17.	Decision Tree is a display of an algorithm.(a) True(b) FalseThe question was asked during a job interview.I need to ask this question from Decision Trees topic in division Learning of Artificial Intelligence
Answer» The CORRECT OPTION is (a) True Best EXPLANATION: NONE.

Discussion

18.	A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.(a) Decision tree(b) Graphs(c) Trees(d) Neural NetworksThis question was posed to me during an online interview.Question is taken from Decision Trees topic in section Learning of Artificial Intelligence
Answer» RIGHT answer is (a) DECISION TREE To explain I WOULD say: REFER the definition of Decision tree.

Discussion

19.	The network that involves backward links from output to the input and hidden layers is called _________(a) Self organizing maps(b) Perceptrons(c) Recurrent neural network(d) Multi layered perceptronI have been asked this question at a job interview.I want to ask this question from Neural Networks topic in portion Learning of Artificial Intelligence
Answer» The correct answer is (c) Recurrent neural network Easy EXPLANATION: RNN (Recurrent neural network) topology involves BACKWARD LINKS from OUTPUT to the input and hidden layers.

Discussion

20.	What is the name of the function in the following statement “A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0”?(a) Step function(b) Heaviside function(c) Logistic function(d) Perceptron functionI had been asked this question in an online interview.My question is based upon Neural Networks topic in section Learning of Artificial Intelligence
Answer» The CORRECT option is (b) HEAVISIDE function Best explanation: Also known as the STEP function – so answer 1 is also right. It is a hard thresholding function, either on or off with no in-between.

Discussion

21.	Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.(a) True – this works always, and these multiple perceptrons learn to classify even complex problems(b) False – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do(c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded(d) False – just having a single perceptron is enoughThis question was addressed to me in unit test.Asked question is from Neural Networks in section Learning of Artificial Intelligence
Answer» Right answer is (C) TRUE – perceptrons can do this but are UNABLE to LEARN to do it – they have to be explicitly hand-coded Easy explanation: NONE.

Discussion

22.	A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.(a) True(b) False(c) Sometimes – it can also output intermediate values as well(d) Can’t sayThe question was asked in homework.I need to ask this question from Neural Networks in chapter Learning of Artificial Intelligence
Answer» Correct option is (a) True For explanation: YES the perceptron WORKS LIKE that.

Discussion

23.	Why is the XOR problem exceptionally interesting to neural network researchers?(a) Because it can be expressed in a way that allows you to use a neural network(b) Because it is complex binary operation that cannot be solved using neural networks(c) Because it can be solved by a single layer perceptron(d) Because itis the simplest linearly inseparable problem that exists.I had been asked this question in exam.Enquiry is from Neural Networks in division Learning of Artificial Intelligence
Answer» Right answer is (d) Because ITIS the SIMPLEST LINEARLY inseparable problem that exists. To elaborate: NONE.

Discussion

24.	Which of the following is not the promise of artificial neural network?(a) It can explain result(b) It can survive the failure of some nodes(c) It has inherent parallelism(d) It can handle noiseI have been asked this question in final exam.Origin of the question is Neural Networks in chapter Learning of Artificial Intelligence
Answer» Right CHOICE is (a) It can explain result Easy EXPLANATION: The artificial NEURAL Network (ANN) cannot explain result.

Discussion

25.	Why are linearly separable problems of interest of neural network researchers?(a) Because they are the only class of problem that network can solve successfully(b) Because they are the only class of problem that Perceptron can solve successfully(c) Because they are the only mathematical functions that are continue(d) Because they are the only mathematical functions you can drawI had been asked this question in exam.My question is taken from Neural Networks in chapter Learning of Artificial Intelligence
Answer» Right option is (B) Because they are the only class of problem that PERCEPTRON can solve successfully Best explanation: Linearly separable problems of INTEREST of neural NETWORK researchers because they are the only class of problem that Perceptron can solve successfully.

Discussion

26.	Neural Networks are complex ______________ with many parameters.(a) Linear Functions(b) Nonlinear Functions(c) Discrete Functions(d) Exponential FunctionsThe question was asked by my school principal while I was bunking the class.My question is taken from Neural Networks in chapter Learning of Artificial Intelligence
Answer» Right choice is (a) LINEAR FUNCTIONS Explanation: NEURAL networks are complex linear functions with MANY PARAMETERS.

Discussion

27.	What is back propagation?(a) It is another name given to the curvy function in the perceptron(b) It is the transmission of error back through the network to adjust the inputs(c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn(d) None of the mentionedThis question was posed to me by my college director while I was bunking the class.This interesting question is from Neural Networks in division Learning of Artificial Intelligence
Answer» RIGHT choice is (c) It is the TRANSMISSION of ERROR back through the NETWORK to allow weights to be adjusted so that the network can learn For EXPLANATION I would say: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

Discussion

28.	A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. What will be the output?(a) 238(b) 76(c) 119(d) 123I have been asked this question at a job interview.I'd like to ask this question from Neural Networks in portion Learning of Artificial Intelligence
Answer» Correct choice is (a) 238 The EXPLANATION: The output is found by multiplying the weights with their respective inputs, summing the results and multiplying with the transfer function. THEREFORE: Output = 2 * (14 + 210 + 35 + 420) = 238.

Discussion

29.	What is an auto-associative network?(a) a neural network that contains no loops(b) a neural network that contains feedback(c) a neural network that has only one loop(d) a single layer feed-forward neural network with pre-processingThis question was posed to me during an online exam.Origin of the question is Neural Networks topic in portion Learning of Artificial Intelligence
Answer» Right choice is (b) a NEURAL network that CONTAINS feedback Best explanation: An auto-associative network is EQUIVALENT to a neural network that contains feedback. The NUMBER of feedback paths(loops) does not have to be one.

Discussion

30.	Which is true for neural networks?(a) It has set of nodes and connections(b) Each node computes it’s weighted input(c) Node could be in excited state or non-excited state(d) All of the mentionedI have been asked this question by my school teacher while I was bunking the class.My question is taken from Neural Networks in chapter Learning of Artificial Intelligence
Answer» CORRECT ANSWER is (d) All of the MENTIONED The EXPLANATION is: All mentioned are the characteristics of neural network.

Discussion

31.	What is perceptron?(a) a single layer feed-forward neural network with pre-processing(b) an auto-associative neural network(c) a double layer auto-associative neural network(d) a neural network that contains feedbackI got this question in quiz.My question is taken from Neural Networks topic in chapter Learning of Artificial Intelligence
Answer» The correct option is (a) a SINGLE layer feed-forward neural network with pre-processing Easiest explanation: The PERCEPTRON is a single layer feed-forward neural network. It is not an auto-associative network because it has no feedback and is not a MULTIPLE layer neural network because the pre-processing stage is not made of neurons.

Discussion

32.	A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalization, the output will be zero when and only when the input is?(a) 000 or 110 or 011 or 101(b) 010 or 100 or 110 or 101(c) 000 or 010 or 110 or 100(d) 100 or 111 or 101 or 001The question was posed to me during an online exam.This intriguing question comes from Neural Networks topic in chapter Learning of Artificial Intelligence
Answer» Correct option is (c) 000 or 010 or 110 or 100 The BEST EXPLANATION: The truth table before generalization is:

Discussion

33.	Which of the following statement is true?(a) Not all formal languages are context-free(b) All formal languages are Context free(c) All formal languages are like natural language(d) Natural languages are context-oriented freeI have been asked this question in an online quiz.This intriguing question comes from Learning topic in portion Learning of Artificial Intelligence
Answer» The CORRECT CHOICE is (a) Not all FORMAL languages are context-free For EXPLANATION I WOULD say: Not all formal languages are context-free.

Discussion

34.	A perceptron is a ______________(a) Feed-forward neural network(b) Backpropagation algorithm(c) Backtracking algorithm(d) Feed Forward-backward algorithmI had been asked this question in a job interview.Enquiry is from Learning in section Learning of Artificial Intelligence
Answer» The correct option is (a) Feed-forward NEURAL network Best explanation: A PERCEPTRON is a Feed-forward neural network with no hidden units that can be representing only LINEAR separable functions. If the data are linearly separable, a simple weight updated RULE can be used to fit the data EXACTLY.

Discussion

35.	Neural Networks are complex ______________with many parameters.(a) Linear Functions(b) Nonlinear Functions(c) Discrete Functions(d) Exponential FunctionsI got this question in exam.The question is from Learning in section Learning of Artificial Intelligence
Answer» The CORRECT option is (b) Nonlinear Functions Explanation: NEURAL networks parameters can be learned from NOISY data and they have been used for thousands of applications, so it varies from PROBLEM to problem and thus USE nonlinear functions.

Discussion

36.	If a hypothesis says it should be positive, but in fact, it is negative, we call it __________(a) A consistent hypothesis(b) A false negative hypothesis(c) A false positive hypothesis(d) A specialized hypothesisI got this question by my college director while I was bunking the class.My question is taken from Learning in portion Learning of Artificial Intelligence
Answer» Right CHOICE is (c) A false positive hypothesis To explain I would say: Consistent hypothesis GO with examples, If the hypothesis SAYS it should be negative but infect it is positive, it is false negative. If a hypothesis says it should be positive, but in fact, it is negative, it is false positive. In a specialized hypothesis we need to have CERTAIN restrict or special CONDITIONS.

Discussion

37.	Computational learning theory analyzes the sample complexity and computational complexity of __________(a) Unsupervised Learning(b) Inductive learning(c) Forced based learning(d) Weak learningI have been asked this question in an internship interview.The question is from Learning in section Learning of Artificial Intelligence
Answer» The correct choice is (b) Inductive learning Easy explanation: COMPUTATIONAL learning theory ANALYZES the SAMPLE complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the HYPOTHESIS LANGUAGE and the ease of learning.

Discussion

38.	In an Unsupervised learning ____________(a) Specific output values are given(b) Specific output values are not given(c) No specific Inputs are given(d) Both inputs and outputs are givenI got this question in an online quiz.My query is from Learning in chapter Learning of Artificial Intelligence
Answer» CORRECT choice is (b) SPECIFIC output values are not given To explain: The PROBLEM of unsupervised learning involves learning PATTERNS in the input when no specific output values are supplied. We cannot expect the specific output to test your result. Here the agent does not know what to do, as he is not aware of the fact what propose system will come out. We can say an ambiguous un-proposed SITUATION.

Discussion

39.	Inductive learning involves finding a __________(a) Consistent Hypothesis(b) Inconsistent Hypothesis(c) Regular Hypothesis(d) Irregular HypothesisI had been asked this question at a job interview.The doubt is from Learning in portion Learning of Artificial Intelligence
Answer» The correct choice is (a) CONSISTENT Hypothesis For explanation: INDUCTIVE learning INVOLVES finding a consistent hypothesis that agrees with examples. The difficulty of the task depends on the chosen REPRESENTATION.

Discussion

40.	How is Fuzzy Logic different from conventional control methods?(a) IF and THEN Approach(b) FOR Approach(c) WHILE Approach(d) DO ApproachThe question was posed to me in quiz.My enquiry is from Learning in chapter Learning of Artificial Intelligence
Answer» The correct choice is (a) IF and THEN Approach The explanation: FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system MATHEMATICALLY.

Discussion

41.	Which of the following is not an application of learning?(a) Data mining(b) WWW(c) Speech recognition(d) None of the mentionedThis question was posed to me during an interview.Enquiry is from Learning topic in chapter Learning of Artificial Intelligence
Answer» The CORRECT ANSWER is (d) None of the MENTIONED Best EXPLANATION: All mentioned options are applications of learning.

Discussion

42.	Which is not a desirable property of a logical rule-based system?(a) Locality(b) Attachment(c) Detachment(d) Truth-FunctionalityI got this question in an internship interview.Question is taken from Learning topic in section Learning of Artificial Intelligence
Answer» Correct option is (B) Attachment Easy explanation: Locality: In logical systems, whenever we have a rule of the form A => B, we can conclude B, given evidence A, without worrying about any other rules. Detachment: Once a logical proof is FOUND for a proposition B, the proposition can be used regardless of how it was derived .That is, it can be detachment from its justification. Truth-functionality: In logic, the truth of COMPLEX sentences can be computed from the truth of the COMPONENTS. However, there are no Attachment properties lies in a Rule-based system. Global attribute defines a PARTICULAR problem space as user specific and changes according to user’s plan to problem.

Discussion

43.	Which of the following is the component of learning system?(a) Goal(b) Model(c) Learning rules(d) All of the mentionedThe question was posed to me in an interview.The above asked question is from Learning in section Learning of Artificial Intelligence
Answer» Right answer is (d) All of the mentioned Explanation: GOAL, model, learning RULES and EXPERIENCE are the COMPONENTS of learning system.

Discussion

44.	Decision trees are appropriate for the problems where ___________(a) Attributes are both numeric and nominal(b) Target function takes on a discrete number of values.(c) Data may have errors(d) All of the mentionedI had been asked this question by my college director while I was bunking the class.My doubt stems from Learning in division Learning of Artificial Intelligence
Answer» Correct ANSWER is (d) All of the mentioned Easy explanation: DECISION TREES can be used in all the CONDITIONS stated.

Discussion

45.	In which of the following learning the teacher returns reward and punishment to learner?(a) Active learning(b) Reinforcement learning(c) Supervised learning(d) Unsupervised learningI got this question in my homework.This intriguing question comes from Learning topic in portion Learning of Artificial Intelligence
Answer» Right answer is (b) REINFORCEMENT learning To explain: Reinforcement learning is the type of learning in which teacher returns reward or PUNISHMENT to learner.

Discussion

46.	Which of the following is an example of active learning?(a) News Recommender system(b) Dust cleaning machine(c) Automated vehicle(d) None of the mentionedI got this question in an online quiz.Enquiry is from Learning in section Learning of Artificial Intelligence
Answer» Correct ANSWER is (a) News Recommender system Easy explanation: In active learning, not only the teacher is available but the LEARNER can ASK suitable perception-action pair examples to improve PERFORMANCE.

Discussion

47.	Automated vehicle is an example of ______(a) Supervised learning(b) Unsupervised learning(c) Active learning(d) Reinforcement learningThis question was addressed to me in an internship interview.My question is from Learning topic in division Learning of Artificial Intelligence
Answer» Correct CHOICE is (a) Supervised learning To explain I would say: In automatic vehicle set of vision inputs and corresponding actions are available to learner HENCE it’s an example of supervised learning.

Discussion

48.	Which of the following is the model used for learning?(a) Decision trees(b) Neural networks(c) Propositional and FOL rules(d) All of the mentionedI have been asked this question in an interview for job.This key question is from Learning in portion Learning of Artificial Intelligence
Answer» Correct option is (d) All of the mentioned The BEST I can EXPLAIN: Decision trees, Neural NETWORKS, PROPOSITIONAL rules and FOL rules all are the models of LEARNING.

Discussion

49.	Which of the following does not include different learning methods?(a) Memorization(b) Analogy(c) Deduction(d) IntroductionI had been asked this question in exam.Query is from Learning topic in portion Learning of Artificial Intelligence
Answer» The correct answer is (d) Introduction The BEST explanation: Different learning methods INCLUDE memorization, ANALOGY and deduction.

Discussion

50.	Factors which affect the performance of learner system does not include?(a) Representation scheme used(b) Training scenario(c) Type of feedback(d) Good data structuresI got this question during an online interview.The above asked question is from Learning in portion Learning of Artificial Intelligence
Answer» Correct option is (d) Good data STRUCTURES Explanation: FACTORS which AFFECT the performance of LEARNER system does not include good data structures.

Discussion

Explore topic-wise InterviewSolutions in .

How many literals are available in top-down inductive learning methods?(a) 1(b) 2(c) 3(d) 4I got this question during an online exam.The question is from Inductive logic programming in section Learning of Artificial Intelligence

Which inverts a complete resolution strategy?(a) Inverse resolution(b) Resolution(c) Trilogy(d) None of the mentionedI have been asked this question in semester exam.This key question is from Inductive logic programming topic in section Learning of Artificial Intelligence

Which produces hypotheses that are easy to read for humans?(a) ILP(b) Artificial intelligence(c) Propositional logic(d) First-order logicThe question was posed to me in a job interview.My query is from Inductive logic programming in chapter Learning of Artificial Intelligence

How many reasons are available for the popularity of ILP?(a) 1(b) 2(c) 3(d) 4This question was posed to me during an online interview.I would like to ask this question from Inductive logic programming in chapter Learning of Artificial Intelligence

End Nodes are represented by __________(a) Disks(b) Squares(c) Circles(d) TrianglesI have been asked this question during an internship interview.This question is from Decision Trees in division Learning of Artificial Intelligence

Chance Nodes are represented by __________(a) Disks(b) Squares(c) Circles(d) TrianglesThis question was posed to me in an online interview.Question is taken from Decision Trees topic in portion Learning of Artificial Intelligence

Choose from the following that are Decision Tree nodes?(a) Decision Nodes(b) End Nodes(c) Chance Nodes(d) All of the mentionedI got this question in my homework.Query is from Decision Trees topic in section Learning of Artificial Intelligence

Decision Nodes are represented by ____________(a) Disks(b) Squares(c) Circles(d) TrianglesThis question was posed to me in homework.Query is from Decision Trees topic in section Learning of Artificial Intelligence

Decision Trees can be used for Classification Tasks.(a) True(b) FalseI have been asked this question in examination.This intriguing question comes from Decision Trees in chapter Learning of Artificial Intelligence

Decision Tree is a display of an algorithm.(a) True(b) FalseThe question was asked during a job interview.I need to ask this question from Decision Trees topic in division Learning of Artificial Intelligence

A perceptron is a ______________(a) Feed-forward neural network(b) Backpropagation algorithm(c) Backtracking algorithm(d) Feed Forward-backward algorithmI had been asked this question in a job interview.Enquiry is from Learning in section Learning of Artificial Intelligence

Neural Networks are complex ______________with many parameters.(a) Linear Functions(b) Nonlinear Functions(c) Discrete Functions(d) Exponential FunctionsI got this question in exam.The question is from Learning in section Learning of Artificial Intelligence

In an Unsupervised learning ____________(a) Specific output values are given(b) Specific output values are not given(c) No specific Inputs are given(d) Both inputs and outputs are givenI got this question in an online quiz.My query is from Learning in chapter Learning of Artificial Intelligence

Inductive learning involves finding a __________(a) Consistent Hypothesis(b) Inconsistent Hypothesis(c) Regular Hypothesis(d) Irregular HypothesisI had been asked this question at a job interview.The doubt is from Learning in portion Learning of Artificial Intelligence

How is Fuzzy Logic different from conventional control methods?(a) IF and THEN Approach(b) FOR Approach(c) WHILE Approach(d) DO ApproachThe question was posed to me in quiz.My enquiry is from Learning in chapter Learning of Artificial Intelligence

Which of the following is not an application of learning?(a) Data mining(b) WWW(c) Speech recognition(d) None of the mentionedThis question was posed to me during an interview.Enquiry is from Learning topic in chapter Learning of Artificial Intelligence

Which is not a desirable property of a logical rule-based system?(a) Locality(b) Attachment(c) Detachment(d) Truth-FunctionalityI got this question in an internship interview.Question is taken from Learning topic in section Learning of Artificial Intelligence

Which of the following is the component of learning system?(a) Goal(b) Model(c) Learning rules(d) All of the mentionedThe question was posed to me in an interview.The above asked question is from Learning in section Learning of Artificial Intelligence

Which of the following is an example of active learning?(a) News Recommender system(b) Dust cleaning machine(c) Automated vehicle(d) None of the mentionedI got this question in an online quiz.Enquiry is from Learning in section Learning of Artificial Intelligence

Automated vehicle is an example of ______(a) Supervised learning(b) Unsupervised learning(c) Active learning(d) Reinforcement learningThis question was addressed to me in an internship interview.My question is from Learning topic in division Learning of Artificial Intelligence

Which of the following is the model used for learning?(a) Decision trees(b) Neural networks(c) Propositional and FOL rules(d) All of the mentionedI have been asked this question in an interview for job.This key question is from Learning in portion Learning of Artificial Intelligence

Which of the following does not include different learning methods?(a) Memorization(b) Analogy(c) Deduction(d) IntroductionI had been asked this question in exam.Query is from Learning topic in portion Learning of Artificial Intelligence