Machine Learning — Summer 2020

Define Learning Problems. Explain the steps for designing a learning system in detail.

What is Machine Learning? List advantages and limitations of the Machine Learning. What is a Version Space? How this concept is used in Candidate Elimination

07 algorithm? Explain in detail.

What is Information Gain (IG)? Explain with example addressing calculation of Information Gain and its usage for selection of attributes.

What is Perceptron? Explain Gradient descent in details.

Write a note on Bayes Theorem.

What is decision tree learning? List and explain issues in decision tree learning.

What is BAYESIAN belief network? Explain with example.

What is Neural Network Representation? Explain with example. Explain the working of CADET System using the concepts of Case Based

07 Reasoning (CBR) algorithm.

Explain K- Nearest Neighbor Learning algorithm.

How sequential covering algorithm works? Explain with suitable example.

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

Explain Q – Learning with example.

What is VC dimension? How it is used?

Explain Naïve Bayes Classifier with example.

Explain FOCL in details.