Machine Learning — Summer 2022

1. Which symbol is used to represent the concept or function to be learned? 2. <x,c(x)> represents ______________ in machine learning. 3. Clustering model is a Supervised model. (T/F) 4. Define machine learning. 5. The most specific possible hypothesis is denoted by ______________. 6. Information gain is the expected ______________ of entropy. 7. Image recognition is an application of machine learning. (T/F)

Explain the steps of machine learning in detail.

What is General-to-Specific Ordering? Explain how is this concept used in Find-Salgorithm?

What is decision tree? When to consider decision tree? With respect to decision tree describe how to decide which attribute should be selected first?

Describe inductive bias and overfitting in decision tree learning.

Define ANN. Describe perceptron and perceptron training rule in detail.

Explain Naive Bayes classifier with example.

What do you mean by gradient descent? Derive the gradient descent rule.

What is the use of Bayes theorem? Explain MAP hypothesis and ML hypothesis with example.

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

How the k-Nearest Neighbour Learning works? Explain.

Explain VC dimension and mistake bound model of learning.

What is Case –Based Reasoning (CBR)? Explain with an example.

Differentiate propositional rules and first-order rules with suitable examples.

Explain the PROLOG-EBG algorithm with example.

Discuss the similarities and differences of FOIL and FOCL algorithms.

What is Reinforcement learning? Explain how it works.