Machine Learning (INDSC4B) — DU B.Sc. (Hons) Instrumentation Previous Year Question Papers

questions in all. Q.1 (a) What is clustering in Machine Learning (b) Explain the importance of dimensionality reduction techniques in machine (c) What is CNN? (2) (d) (e) Define the types of errors that can occur during hypothesis testing of the data. Describe any 2 of applications of an ANN algorithm. (f) What is overfitting in decision trees? (2) (c) Discuss any four applications of Machine learning. (4) (4) Q.3 (a) (b) (c) Describe classification separating hyperplanes and their significance in machine learning. Define version spaces in the context of machine learning. Explain how version spaces represent the set of all hypotheses consistent with the training data. (4) Q.4 (a) Discuss the steps to perform hypothesis testing in machine learning. (b) Explain the working of an ANN with the help of suitable diagram. (6) Q.5 (a) Explain the Id3 algorithm for constructing decision trees. What are its disadvantages and how can we control it? (b) What is KNN classifier? Explain with an example (6) (6) Q.6 (a) Explain the SVM algorithm and how are they classified? (b) Differentiate between Maximum Likelihood Estimation (MLE) and Bayesian Parameter Estimations Models. (6) Q.7 (a) Discuss the ensemble methods in machine learning. (b) Discuss the methods used for evaluation measures used for classification techniques. (c) Explain the Gaussian Mixture Model (GMM) for clustering and density estimation.