Artificial Intelligence and Machine Learning Archives - Page 8 of 9

Appropriate Problems for Artificial Neural Networks

Appropriate Problems for Artificial Neural Networks for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial Most appropriate for problems where, Instances have many attribute-value pairs: The target function to be learned is defined over instances that can be described by a vector of predefined features. Target function output may be discrete-valued, real-valued, or […]

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Perceptron Training Rule for Linear Classification

Machine Learning / Vidyashri M H

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

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18CS76 Machine Learning Laboratory VTU ML Lab

Machine Learning / Vidyashri M H

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

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Candidate Elimination Algorithm in Python

Machine Learning / Vidyashri M H

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

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FIND S Algorithm in Python

Machine Learning / Vidyashri M H

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

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Plant leaf infection detection Project

Image Processing Projects, Machine Learning Projects / Vidyashri M H

Plant leaf infection detection Project for final year students In this project machine learning and image processing techniques are used to detect and classify the plant leafs as infectious and non-infectious. Video Demonstration Model Building Deployment Streamlit Application The project is tested on different plant leaf datasets such as Bell Pepper, Maize, and tomato leaf

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Machine Learning Question With Answers Module 5

Machine Learning / 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.

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Machine Learning Question With Answers Module 4

Machine Learning / Vidyashri M H

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

Machine Learning / Vidyashri M H

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

Machine Learning / Vidyashri M H

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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Artificial Intelligence and Machine Learning

Appropriate Problems for Artificial Neural Networks

Perceptron Training Rule for Linear Classification

18CS76 Machine Learning Laboratory VTU ML Lab

Candidate Elimination Algorithm in Python

FIND S Algorithm in Python

Plant leaf infection detection Project

Machine Learning Question With Answers Module 5

Machine Learning Question With Answers Module 4

Machine Learning Question With Answers Module 3

Machine Learning Question With Answers Module 2

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