Fundamentals of Machine Learning — Summer 2023

Define following terms with respect to machine learning:

precision (ii) overfitting (iii) concept learning

What is machine learning? What are key tasks of machine Learning?

List and explain the types of machine learning in brief.

Discuss candidate-elimination algorithm.

What is Gibbs Algorithm?

What is perceptron? What is perceptron training rule? What are its limitations and by which rule these limitations can be overcome?

Explain how Naïve Bayes classifier is used in text classification.

Write two strength and weakness of Bayesian classifier.

What is Naïve Bayes classifier? Why is it named so?

What is inductive bias in a decision tree? How to avoid overfitting? Is there any effect on classification due to bias?

Define following terms in decision tree. 1) Leaf node 2) Entropy 3) Information gain

Consider the following confusion matrix of the win/loss prediction of cricket match. Calculate model accuracy and error rate for the same. Actual Win Actual Loss Predicted Win 82 Predicted Loss 3

Write ID3 Decision Tree Algorithm and explain with suitable example.

Briefly explain Multilayer Feed Forward NN.

Why do we require Pruning in Decision Trees? Explain.

Describe CART. Explain with suitable example.

What is threshold activation function in ANN?

Design a multi-layer perceptron to implement A XOR B.

Describe, in details, the process of adjusting the interconnection weights in a multi-layer neural network.

Briefly explain the properties of Gini Impurity in decision tree.

Discuss application of ANN in face recognition.

Explain Confusion Matrix with respect to detection of “Spam e-mails”.

Briefly explain EM Algorithm.

What is recommender system? How machine learning methods can be useful in it?

Explain the concept of Prior, Posterior, and Likelihood with an example.