Machine Learning — Winter 2018

Write a short note on-

Advantages of machine learning algorithms (ii) Bayesian Belief Network

Write a short note on-

Concept learning (ii) Artificial Neural Network(ANN)

Design a learning system step by step for checker problem.

For the given data set find out the hypothesis using Candidate Elimination algorithm. Sky Temp Humidity Wind Water Forecast Enjoy Sport Sunny Warm Normal Strong Warm Same Yes Sunny Warm High Strong Warm Same Yes Rainy Cold High Strong Warm Change No Sunny Warm High Strong Cool Change Yes

Explain ID3 decision tree algorithm with suitable example.

What is feed forward network? Discuss Backpropogation algorithm in detail.

Write formula for Bayes theorem. Explain Bayes theorem with suitable example.

Define perceptron. Write the formula for calculating errors at each output unit and updating the weights.

Explain Naïve Bayes classification algorithm.

How Case based reasoning helps in learning? Explain with suitable example.

What is sample complexity for Probably Approximately Correct framework (PAC)? How it is different from mistake bound frame work?

Compare lazy and eager algorithm. Explain any one of lazy algorithm with example.

Explain Qlearning with example.

What do you understand by explanation based learning explain with PROLOG EGP algorithm.

Describe sequential covering algorithm.

Compare supervised, unsupervised and reinforcement algorithm with suitable example.

Discuss how First Order Horn clauses help in learning.