Machine Learning — Winter 2020

Answer the following questions: 1) What is regression? 2) What do you mean by unsupervised learning? 3) How do machine learn?

Give the comparison among supervised, unsupervised and reinforcement learning.

Explain the different issues in machine learning.

Relate Inductive bias with respect to Decision tree learning.

What are the advantages and disadvantages of the K-nearest neighbor learning?

Explain the concept of regression in machine learning.

What do you mean by Recommender System?

Explain Locally Weighted Linear Regression.

Give decision tree for the following set of training examples. Day Outlook Temperature Humidity Wind Play_Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No

Define Concept and Concept Learning.

Is it possible to use Naïve Bayes classifier for continues numeric data? If so how?

Consider the following set of training examples. 1) What is the entropy of this collection of training example with respect to the target function classification?1 2) What is the information gain of a relative to these training2 examples? Instance Classification a a12 1 + T T 2 + T T 3 - T F 4 + F F 5 - F T 6 - F T

What is linearly inseparable problem? What is the role of the hidden layer?

What are the types of problems in which Artificial Neural Network can be applied?

What is Cost function in BackPropagation? Discuss Back propagation algorithm.

What is artificial neural network (ANN)?

Under what conditions the perceptron rule fails and it becomes necessary to apply the delta rule.

Explain the Bayesian Belief Network with suitable example in detail.

Define (i) Prior Probability (ii) Conditional Probability (iii) Posterior Probability.

Explain the Gradient Search to Maximize Likelihood in a neural Net.

Explain Bayesian belief network and conditional independence with example.

Define Bayesian theorem? What is the relevance and features of Bayesian theorem?

Discuss Maximum Likelihood and Least Square Error Hypothesis.

Explain the concept of EM Algorithm. Discuss what the Gaussian Mixtures are?