Perceptron Training Rule for Linear Classification for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial A perceptron unit is used to build the ANN system. A perceptron takes a vector of real-valued inputs, calculates a linear combination of these inputs, then outputs a 1 if the result is greater than some threshold and […]
Perceptron Training Rule for Linear Classification Read More »
Machine Learning Laboratory VTU ML Lab – 18CS76, 17CS73, 18CS71 Machine Learning Laboratory – 18CS76 (VTU ML Lab) covers the machine learning algorithms such as Find-S algorithms, Candidate elimination algorithm, Decision tree (ID3) algorithm, Backpropagation Algorithm, Naïve Bayesian classifier for text classification, Bayesian Network, k-Means and EM clustering Algorithm, k-Nearest Neighbour Algorithm, Locally Weighted Regression
Python Program to Implement Candidate Elimination Algorithm to get Consistent Version Space Exp. No. 2. For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm in python to output a description of the set of all hypotheses consistent with the training examples. Candidate Elimination Algorithm Machine
Python Program to Implement FIND S Algorithm – to get Maximally Specific Hypothesis Exp. No. 1. Implement and demonstrate the FIND-S algorithm in Python for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file Find-S Algorithm Machine Learning 1. Initilize h to
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
Candidate Elimination Algorithm in Machine Learning Candidate Elimination Algorithm is used to find the set of consistent hypothesis, that is Version spsce. Click Here for Python Program to Implement Candidate Elimination Algorithm to get Consistent Version Space Video Tutorial of Candidate Elimination Algorithm Solved Example – 3 Algorithm: For each training example d, do: If
Candidate Elimination Algorithm Solved Example – 3 Read More »
