mllab's Introduction

All programs are updated.

1.

Implement and demonstrate the FIND-S algorithm for
finding the most specific hypothesis based on agivenset of
training datasamples. Read the training data from a .CSV
file.

2.

For a given set of training data examples stored in a .CSV
file,implement anddemonstratethe Candidate-Elimination
algorithm tooutputadescriptionofthesetofallhypotheses
consistentwiththetrainingexamples.

3.

Writeaprogramtodemonstratetheworkingofthedecision
treebased ID 3 algorithm .Useanappropriatedatasetfor
building the decision tree and apply this knowledge to
classifyanewsample.

4.

Build an Artificial Neural Network by implementing the
Backpropagation algorithm and test the same using
appropriatedatasets.

5.

Writeaprogramtoimplementthe naïveBayesianclassifier
forasampletrainingdatasetstoredasa.CSVfile.Compute
theaccuracyoftheclassifier,consideringfewtestdatasets.

6.

Assumingasetofdocumentsthatneedtobeclassified,use
the NaïveBayesianClassifier modeltoperformthistask.
Built-inJavaclasses/APIcanbeusedtowritetheprogram.
Calculatetheaccuracy,precision,andrecallforyourdataset.

7.

Write a program to construct a Bayesian network
consideringmedicaldata.Usethismodeltodemonstratethe
diagnosisofheartpatientsusingstandardHeartDiseaseData
Set.YoucanuseJava/PythonMLlibraryclasses/API.

8.

Apply EMalgorithm toclusterasetofdatastoredina.CSV
file.Use the same data set forclusteringusing k-Means
algorithm .Comparetheresultsofthesetwoalgorithmsand
comment on the quality of clustering. You can add
Java/PythonMLlibraryclasses/APIintheprogram.

9.

Write a program to implement k-Nearest Neighbour
algorithm toclassifytheirisdataset.Printbothcorrectand
wrongpredictions. Java/PythonMLlibraryclasses canbe
usedforthisproblem..

10.

Implement the non-parametric Locally Weighted
Regression algorithm in order to fit data points. Select
appropriatedatasetforyourexperimentanddrawgraphs.

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