Machine Learning — Unit 1
Introduction to Machine Learning, Concept Learning, Candidate Elimination, Perceptron and Linear Regression
ASupervised Learning
BReinforcement Learning
CUnsupervised Learning
DSequential Programming
Correct AnswerD
AInput-output pairs
BOnly input features
COnly outputs
DRandom samples
Correct AnswerA
ASupervised Learning
BReinforcement Learning
CUnsupervised Learning
DDeep Learning
Correct AnswerC
AError correction
BRewards and punishments
CData clustering
DLinear regression
Correct AnswerD
AA data structure
BA target concept
CA clustering algorithm
DAn unsupervised pattern
Correct AnswerB
ATraining and test boundaries
BFeature and target boundaries
CSpecific and general hypotheses
DInput and output boundaries
Correct AnswerC
AOnly general hypotheses
BAll hypotheses consistent with training data
COnly specific hypotheses
DInconsistent hypotheses
Correct AnswerB
AClassification
BRegression
CClustering
DDimensionality reduction
Correct AnswerA
AK-means
BPerceptron
CNaive Bayes
DPCA
Correct AnswerB
AFind simplest hypothesis consistent with data
BMemorize data
CBuild complex model
DReduce memory
Correct AnswerA
AMapping from input to output
BRandom guess
CNeural weight
DDataset
Correct AnswerA
AAll generalizations
BIgnores negatives
CCovers only observed positives
DMost general
Correct AnswerC
AAll hypotheses
BConsistent hypotheses
CMinimum error hypotheses
DGeneral only
Correct AnswerB
ASingle hypothesis
BMost general & most specific boundary
CRandom models
DRegression equations
Correct AnswerB
ASupervised Learning
BUnsupervised Learning
CReinforcement Learning
DSemi-supervised Learning
Correct AnswerA
AForward
BBackward
CSide
DDiagonal
Correct AnswerD
AReinforcement
BDimensionality reduction
CClustering
DClassification
Correct AnswerD
A((Sunny, Warm, Strong, Humid)
B((Sunny, ?, ?, ?)
C((?, ?, ?, ?)
D((φ, φ, φ, φ)
Correct AnswerC
ALinear combination of inputs
BQuadratic
CProduct
DSquared sum
Correct AnswerA
ASpecific
BGeneral
CNull
DAlternative
Correct AnswerB
AClustering
BLinear classification
CDimensionality reduction
DProbability estimation
Correct AnswerB
AAdjusts weights
BStops
CResets
DIgnores
Correct AnswerA
ASupervised
BManual
CMechanical
DStatic
Correct AnswerA
ANo output
BRandom data
CLabeled data
DUnlabeled
Correct AnswerC
AAxon
BDendrite
CSynapse
DNucleus
Correct AnswerB
ATraditional
BMachine learning
CManual
DStatic
Correct AnswerB
ARegression
BClassification
CClustering
DSorting
Correct AnswerB
AOptimization
BSearch problem
CGraph
DSorting
Correct AnswerB
Correct AnswerB
AAll hypotheses
BConsistent hypotheses
CRandom
DNone
Correct AnswerB
AMcCarthy
BTom Mitchell
CRosenblatt
DHebb
Correct AnswerB
ANon-linear
BStep function
CGaussian
DPolynomial
Correct AnswerB
ANon-linear
BRandom
CLinear
DClustered
Correct AnswerC
ACurve
BCircle
CStraight line
DHyperbola
Correct AnswerC
AClassification
BPrediction
CClustering
DAssociation
Correct AnswerB
AAbsolute error
BSquared error
CClassification error
DEntropy
Correct AnswerB
AContinuous
BProbabilistic
CBinary
DMulticlass
Correct AnswerC
Fill in the Blanks
Machine Learning — Unit 1
Answerlabeled
Answer(φ, φ, φ, φ)
Answerhypothesis
Answernegative
Answerconsistent
Answerdiscrete
Answerexperience
Answerunlabeled
Answerneuron
Answerinput
Answeroutput
Answerlearning
Answerconcept learning.
AnswerS and G
Answerlinear
Answercontinuous
Answermemorizes
Answergeneralization
AnswerInformation Gain
Answerconcept learning
Answerconsistent
Answerlinearly separable
Answerexperience
Answerconcept learning
Answercontinuous
Answerconcept learning
Answerversion space