Machine Learning2 - UNIT 2 : Decision Tree and Artificial Neural Network Questions

UNIT-2 

Short Answer Questions 

•  Define Entropy and Information Gain. 
•  What is the purpose of Entropy and Information Gain in ID3. 
•  Define Overfitting. 
•  Define Decision Tree. 
•  Define Perceptron. 
•  Define neural networks 
•  What is backpropagation 
•  Define Neuron. 

Long Answer Questions 

•  Appropriate Problems for Decision Tree Learning. 
•  Construct the decision tree for the following dataset. 

•  What is the entropy of this collection of training examples with respect to the target function classification? 
•  What is the information gain of a_2 relative to these training examples. 
•  Draw the decision tree for the given dataset. 
•  List the different issues in Decision Tree Learning. Explain. 
•  Define Overfitting. How to overfitting data in decision tree construction? 
•  How can you incorporate the continuous values? Explain. 
•  Give decision trees to represent the following Boolean functions: 

 A ∧¬ B 

 A ∨ [B ∧ C] 

 A XOR B 

 [A∧ B] ∨ [C ∧ D] 

•  Define Entropy and Information Gain in ID3 with an example? 
•  Explain ID3 algorithm. 
•  What is Artificial Neural Network? What are the type of problems in which Artificial Neural Network can be applied. 
•  Explain the concept of a Perceptron with a neat diagram. 
•  Discuss the Perceptron training rule. 
•  Under what conditions the perceptron rule fails and it becomes necessary to apply the delta rule What do you mean by Gradient Descent? 
•  Derive the Gradient Descent Rule. 
•  What are the conditions in which Gradient Descent is applied. What are the difficulties in applying Gradient Descent. 
•  Differentiate between Gradient Descent and Stochastic Gradient Descent Define Delta Rule. 
•  Derive the Backpropagation rule considering the training rule for Output Unit weights and Training Rule for Hidden Unit weights Write the algorithm for Back propagation. 
•  Explain how to learn Multilayer Networks using Gradient Descent Algorithm.
•  What is Squashing Function? 
•  How a multi layered network learns using a gradient descent algorithm? 
• Discuss Discuss in detail about representation of Neural Networks Explain back-propagation algorithm in detail. 
•  Explain the Back propagation learning algorithm and its limitations. 
•  Discuss the issues related to neural network learning Construct decision tree for the following dataset using ID3.
  
  
  
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Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes

UNIT – II (B)

Artificial Neural Networks

• Introduction
• Neural Network Representation
• Appropriate Problems for Neural Network Learning
• Perceptron’s
•  Multilayer Networks
• The Back-Propagation Algorithm

 

Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs

UNIT – II (B)

Artificial Neural Networks

Introduction

Neural Network Representation

Appropriate Problems for Neural Network Learning

Perceptron’s

 Multilayer Networks and

the Back-Propagation Algorithm.

Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs

UNIT-2 (A) 

Decision Tree Learning 

 Decision Tree Learning :

• Introduction
• Decision Tree Representation
• Appropriate Problems for Decision Tree Learning
• The Basic Decision Tree Learning Algorithm
• Issues In Decision Tree Learning

 

Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning Notes

UNIT-2 (A)

Decision Tree Learning 

 Decision Tree Learning:

• Introduction
• Decision Tree Representation
• Appropriate Problems for Decision Tree Learning
• The Basic Decision Tree Learning Algorithm
• Issues In Decision Tree Learning

 

Machine Learning 2 - UNIT-1 Questions

UNIT-1 

Short Answer Questions 

1. What do you mean by Concept Learning? Explain 

2. List the applications of machine learning. 

3. Define Machine learning 

4. How is Candidate Elimination algorithm different from Find-S Algorithm 

5. What do you mean by a well –posed learning problem? 

 6. Explain the important features that are required to well –define a learning problem. 

7. What is the difference between Find-S and Candidate Elimination Algorithm 

 Long Answer Questions 

1. List The Different Well-Posed Learning Problems. Explain. 

2. List The Different Steps in Designing a Learning System 

3. Perspectives And Issues in Machine Learning 

4. Explain Machine Learning Techniques 

 5. What are the different types of a Machine Learning models? 

6. Describe in detail all the steps involved in designing a learning system. 

 7. What are the basic design issues and approaches to machine learning? 

8. How do you design a checkers learning problem 

9. Explain the various stages involved in designing a learning system 

 10. Explain find –S algorithm with given example. Give its application. 

 11. Candidate Elimination Algorithm for the following dataset. 

 12. Trace the Candidate Elimination Algorithm for the hypothesis space H’ given the sequence of training examples from below table. H’= < ?, Cold, High, ?,?,?>v 

 13. Differentiate between Supervised, Unsupervised and Reinforcement Learning 

 14. Explain the List Then Eliminate Algorithm with an example 

15. What do you mean by Concept Learning? 

16. What are supervised, semi-supervised and unsupervised learning? 

17. Discuss the perspective and issues in machine learning.

Machine Learning 2- UNIT-1 (B) Concept Learning and the General to Specific Ordering PPTs

UNIT-1 (B) 

 Concept Learning and the General to Specific Ordering 

 Concept Learning and the General to Specific Ordering 

• Introduction
• A Concept Learning Task
• Concept Learning as Search
• Find-S: Finding a Maximally Specific Hypothesis
• Version Spaces and the Candidate Elimination Algorithm