What is Machine learning? Describe the types of learning.
What is clustering? Describe the approaches used for clustering.
What is sensitivity and specificity? Describe Confusion matrix with example.
What is linear regression? Explain gradient descent algorithm.
Describe training set, validation set and test set. Differentiate between training and testing.
Describe line of best fit.
What is naive bayes algorithm? How does it work? What are its merits and demerits?
Attempt any TWO questions
[2x10=20]How does k-means algorithm work? Suppose following data k = 2 use k-means algorithm to cluster O1(1, 1.5), O2(1, 4.5), O3(2, 1.5), O4(2, 3.5), O5(3, 2.5), O6(3, 4)
What is perceptron? Explain back propagation algorithm with suitable example.
How does SVM works? Describe functional margin, geometric margin and optimum margin classifier. Show the relationship between functional and geometrical margin.