Vidyashri M H

17CS73 Machine Learning Question With Answers Module 5 MODULE 5 – EVALUATING HYPOTHESIS, INSTANCE BASED LEARNING, REINFORCEMENT LEARNING 1. Explain the two key difficulties that arise while estimating the Accuracy of Hypothesis. 2. Define the following terms a. Sample error b. True error c. Random Variable d. Expected value e. Variance f. standard Deviation 3.

17CS73 Machine Learning Question With Answers Module 4 MODULE 4 – BAYESIAN LEARNING 1. Define the Bayesian theorem? What is the relevance and features of the Bayesian theorem? Explain the practical difficulties of the Bayesian theorem. 2. Define is Maximum a Posteriori (MAP) Maximum Likelihood (ML) Hypothesis. Derive the relation for hMAP and hML using

17CS73 Machine Learning Question With Answers Module 3 MODULE 3 – ARTIFICIAL NEURAL NETWORKS 1. What is Artificial Neural Network? 2. Explain appropriate problem for Neural Network Learning with its characteristics. 3. Explain the concept of a Perceptron with a neat diagram. 4. Explain the single perceptron with its learning algorithm. 5. How a single

17CS73 Machine Learning Question With Answers Module 2 MODULE 2 – DECISION TREE LEARNING 1. What is decision tree and decision tree learning? 2. Explain representation of decision tree with example. 3. What are appropriate problems for Decision tree learning? 4. Explain the concepts of Entropy and Information gain. 5. Describe the ID3 algorithm for

17CS73 Machine Learning Question With Answers Module-1 MODULE 1 – INTRODUCTION AND CONCEPT LEARNING 1. Define Machine Learning. Explain with examples why machine learning is important. 2. Discuss some applications of machine learning with examples. 3. Explain how some disciplines have influenced machine learning. 4. What is well-posed learning problems. 5. Describe the following problems