a) How does supervised learning differ from unsupervised learning? Discuss. (Unit 1)

b) Briefly explain the need of Inductive Bias in decision Tree Learning. (Unit 1)

a) What is Principal Component Analysis (PCA) in machine learning? How does PCA help in reducing the dimensionality of data? (Unit 1)

b) Evaluate the effectiveness of biologically inspired neural network architectures in solving complex machine learning tasks. (Unit 2)

a) What is the back propagation algorithm in neural networks? How does it compute gradients to update the weights of a neural network? (Unit 2)

b) Discuss how the Multilayer Perceptron algorithm compute outputs from inputs through its interconnected layers? (Unit 2)

a) What is Naive Bayes classification in machine learning? Describe the fundamental principle behind Naive Bayes and how it calculates probabilities. (Unit 3)

b) Discuss about linear regression and derive the Individual error and Minimization functions. (Unit 3)

a) Describe the basic concept of a Random Forest and how it combines multiple decision trees for classification. (Unit 3)

b) List the applications of clustering and identify advantages and disadvantages of clustering algorithm. (Unit 4)

a) Explain the Expectation-Maximization (EM) algorithm used to train Gaussian Mixture Models. (Unit 4)

b) What are evolutionary optimization techniques in machine learning? Provide an overview of common evolutionary algorithms used for optimization tasks. (Unit 4)

a) Describe key elements of experimental design such as hypothesis formulation, variable manipulation, and control. (Unit 5)

b) What is the significance of comparing machine learning models over multiple datasets? (Unit 5)

Write a short note on any two of the following:

a) Shrinkage Methods (Unit 1)

b) Vanishing and Exploding Gradients (Unit 2)

c) Issues in decision trees (Unit 3)

d) Hypothesis testing (Unit 5)