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)
[7 marks]Explain the steps of machine learning in detail.
[7 marks]What is General-to-Specific Ordering? Explain how is this concept used in Find-Salgorithm?
[7 marks]What is decision tree? When to consider decision tree? With respect to decision tree describe how to decide which attribute should be selected first?
[7 marks]Describe inductive bias and overfitting in decision tree learning.
[7 marks]Define ANN. Describe perceptron and perceptron training rule in detail.
[7 marks]Explain Naive Bayes classifier with example.
[7 marks]What do you mean by gradient descent? Derive the gradient descent rule.
[7 marks]What is the use of Bayes theorem? Explain MAP hypothesis and ML hypothesis with example.
[7 marks]What is Probably Approximately Correct framework (PAC)? Explain in detail.
[7 marks]How the k-Nearest Neighbour Learning works? Explain.
[7 marks]Explain VC dimension and mistake bound model of learning.
[7 marks]What is Case –Based Reasoning (CBR)? Explain with an example.
[7 marks]Differentiate propositional rules and first-order rules with suitable examples.
[7 marks]Explain the PROLOG-EBG algorithm with example.
[7 marks]Discuss the similarities and differences of FOIL and FOCL algorithms.
[7 marks]What is Reinforcement learning? Explain how it works.
[7 marks]