Engineering BE Computer Engineering Sem 6 May 2019 2019 Machine Learning Question Paper - Mumbai University | munotes

Questions asked in this paper

1. Q2 (a) Explain the steps of developing Machine Learning applications 10 marks
   • (b) Consider Markov chain model for ‘Rain’ and ‘Dry’ is shown in following figure. Two states: ‘Rain’ and ‘Dry’. Transition probabilities: P(‘Rain’|‘Rain’) = 0.2, P(‘Dry’|‘Rain’) Calculate a probability of a sequence of states ‘Rain’, ‘Rain’, ‘Dry’} 10
   • (a) Minimize f(x1,x2) = 4x, — + 2x? + With starting point X, = { using the steepest descent method 10
2. Q3 (b) Explain following terms Initial hypothesis, Expectation step and Maximization step w.r.t E-M algorithm. Explain How Initial hypothesis converges to optimal solution? (You may explain it with an example) 10 marks
3. Q4 (a) Why Dimensionality reduction is an important issue? Describe the steps to reduce dimensionality using Principal Component Analysis method by clearly stating mathematical Paper Subject Code: 88905 Elective - If Machine Learning 10 marks
   • (b) For the following data, Calculate Gini indexes and determines which attribute is root attribute and generate two level deep decision tree 10
4. Q5 (a) Explain following terms w.r.t Bayes’ theorem with proper examples. 10 marks
   • (a) Independent probabilities
   • (b) Dependent probabilities
   • (c) Conditional Probability Define Bays theorem based on these probabilities
   • (b) Draw and discuss the structure of Radial Basis Function Network. How RBFN can be used to
5. Q6 Attempt any four 20 marks
   • (a) Illustrate Support Vector machine with neat labeled sketch and also show how to derive
   • (b) Differentiate: Derivative Based and Derivative free optimization techniques
   • (c) Explain how regression problem can be solved using Steepest descent method. Write down the
   • (d) Write Short note on ISA and compare it with PCA

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