a) Explain Principle component analysis with Examples. (Unit 1)

b) Define Machine Learning and explain different issues of Machine Learning. (Unit 1)

a) Explain Multi layer perception model with neat diagram. (Unit 2)

b) Explain parallel processing perception learning in neural networks with neat diagram. (Unit 2)

a) Explain Random Forest algorithm and explain different regression problems. (Unit 3)

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

a) Use K Means clustering to cluster the following data into two groups. Assume cluster centroid are $m_{1}=2$ and $m_{2}=4$ 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) Define Neural Networks and Explain different functions used in Neural Networks. (Unit 2)

b) Explain Logistic regression with examples. (Unit 3)

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) Explain adaptive hierarchical clustering. (Unit 4)

a) What is cross validation and Explain resampling methods in machine learning? (Unit 5)

b) Explain different shrinkage methods in machine learning. (Unit 1)

Write short notes on the following:

a) Hypothesis testing (Unit 5)

b) Subset selection (Unit 1)

c) Inductive bias (Unit 1)

d) Simulation (Unit 2)