Machine Learning — Summer 2024

Answer all the questions below. 1. Give two examples of applications of ML in speech recognition. [1] 2. Define version space. [1] 3. Differentiate training data set and testing data set. [2] 4. Write the Bayes’ theorem in probabilistic terms. [1] 5. How the instance based learning methods (IBL) works? Explain. [2]

Explain step by step process of machine learning with respect to any example of your choice.

What is concept learning? How Find-Sis used in concept learning?

What is Information Gain (IG)? How is it used in selecting the best attribute in a decision tree? Explain with an example.

What is over-fitting in decision trees? How to avoid it? Explain.

Discuss Back propagation algorithm in detail.

Differentiate Gradient descent and Stochastic gradient descent. Write the Gradient Descent algorithm.

What do you mean by perceptron? Explain the perceptron training rule for learning the weights.

What is VC dimension? How it is used? Explain.

What is Probably Approximately Correct framework (PAC)? Explain in detail.

Explain Bayesian Belief Network using an example.

Explain K- Nearest Neighbor Learning algorithm.

Explain Naive Bayes classification algorithm.

What is the difference between propositional and first-order logic. Write the sequential covering algorithm.

Explain Explanation based learning with help of PROLOG-EBG algorithm.

Explain First-order Inductive Learner (FOIL) algorithm.

Briefly write about the Reinforcement learning and MDP model.