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following regression technique with an example. i) Linear and multiple regression. ii) Non linear regression. 10. 4. a)
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III Semester M.E. (Bio-informatics) Degree Examination, January 2015 (2K13 Scheme) BI31 : MACHINE LEARNING Time : 3 Hours

Max. Marks : 100

Instruction : Answer any five full questions. 1. a) What is machine learning ? Explain how it can be used for association rule mining and classification.

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b) What are the differences between supervised learning and unsupervised learning ? Explain the process of supervised learning from examples.

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2. a) What is vapnik Chervonenkis (VC) dimension ? Explain the probably approximately correct (PAC) learning.

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b) Explain EM/GEM algorithms by taking some datasets as an example in detail.

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3. a) Explain the process of classification using Naive Bayes classifier.

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b) What are the differences between classification and prediction ? Explain the following regression technique with an example. i) Linear and multiple regression ii) Non linear regression.

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4. a) What are Bayesian belief nerworks ? Explain the different steps used for training Bayesian belief networks. b) Write and explain Apriori algorithm for learning Associations.

10 10 P.T.O.

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5. a) Use FP-growth algorithm to find the frequent patterns for the following data set with min sup = 50%. 10 TID

Items-brought

T100

{K, A, D, B}

T200

{D, A, C, E, B}

T300

{C, A, B, E}

T400

{B, A, D}

b) Explain the techniques for finding similarity and dissimilarity for different types of data sets in machine learning. 10 6. a) Explain with an example, the process of clustering usage K-means clustering algorithm.

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b) Explain how the support vector machine (SVM) can be used for classification.

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7. a) What are Markov models ? Explain how Hidden Markov models (HMM) can be used for draining DNA sequences ? b) List and explain the different graphical model examples briefly. 8. Write short notes on :

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(5×4=20)

i) Parametric models ii) EM/GEM algorithm iii) Stochastic grammars and Linguistics iv) Probabilistic modeling. _____________