Machine Learning

Machine Learning Question With Answers Module 5

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. …

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Machine Learning Question With Answers Module 4

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 …

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Machine Learning Question With Answers Module 3

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 …

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Machine Learning Question With Answers Module 2

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 …

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Machine Learning Question With Answers Module 1

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 the machine learning. 4. What is well- posed learning problems. 5. Describe the …

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Version Space and List-Then-Eliminate Algorithm

Consistent Hypothesis, Version Space and List-Then-Eliminate Algorithm An hypothesis h is said to be consistent hypothesis with a set of training examples D iff  h(x) = c(x) for each example in D, Video Tutorial on Consistent Hypothesis, Version Space and List-Then-Eliminate Algorithm For Example: Example Citations Size InLibrary Price Editions Buy 1 Some Small No …

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FIND S Algorithm – Maximally Specific Hypothesis Solved Example

FIND S Algorithm – Maximally Specific Hypothesis Solved Example FIND S Algorithm is used to find the Maximally Specific Hypothesis. Using the Find-S algorithm gives a single maximally specific hypothesis for the given set of training examples. Find-S Algorithm Machine Learning 1. Initilize h to the most specific hypothesis in H 2. For each positive …

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