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.
10
b) What are the differences between supervised learning and unsupervised learning ? Explain the process of supervised learning from examples.
10
2. a) What is vapnik Chervonenkis (VC) dimension ? Explain the probably approximately correct (PAC) learning.
10
b) Explain EM/GEM algorithms by taking some datasets as an example in detail.
10
3. a) Explain the process of classification using Naive Bayes classifier.
10
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.
10
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.
10
b) Explain how the support vector machine (SVM) can be used for classification.
10
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 :
