a) List and Explain perspectives and issues of Machine Learning. (Unit 1)

b) Distinguish between supervised learning and Unsupervised learning with example. (Unit 1)

a) Define Back propagation and write an algorithm for Back Propagation with examples. (Unit 2)

b) What is a Perceptron? Explain the working of a perceptron with a neat diagram. (Unit 2)

a) Explain how Support Vector Machine can be used for classification of linearly separable data. (Unit 3)

b) Define Decision tree. Explain Decision tree algorithm with example. (Unit 3)

a) Use K Means clustering to cluster the following data in to two groups. Assume cluster centroid are $m1=2$ and $m2=4$. The distance function used is Euclidean distance {2, 4, 10, 12, 3, 20, 30, 11, 25}. (Unit 4)

b) Explain Expectation Maximization algorithm with example. And also explain why we need it? (Unit 4)

a) Explain hypothesis testing with examples. (Unit 5)

b) Explain resample methods of machine learning. (Unit 5)

a) What is Linear Regression? Explain in detail with example and list all the assumptions to be met before starting with Linear Regression. (Unit 3)

b) Differentiate between regression and classifications. (Unit 3)

a) What is Gaussian Mixture density estimation with example. (Unit 4)

b) Explain different algorithms for dimension reduction with example. (Unit 1)

Write short notes on the following:

a) Cross validation (Unit 5)

b) Training and validation (Unit 2)

c) Cluster (Unit 4)

d) Factors. (Unit 5)