Machine Learning Laboratory (21AIL66)
COURSE DESCRIPTION
Welcome to the Machine Learning Laboratory, a course designed to equip learners with the practical skills and knowledge needed to excel in the field of machine learning. This course covers the practical algorithms for machine learning from a variety of perspectives. We cover topics such as FIND-S, Candidate Elimination Algorithm, Decision Tree (ID3 Algorithm), Backpropagation Algorithm, Naïve Bayesian Classifier, Bayesian Network, k-Means Algorithm, k-Nearest Neighbour Algorithm, Locally Weighted Regression Algorithm and Support Vector Machine Algorithm
Welcome to the Machine Learning Laboratory, a course designed to equip learners with the practical skills and knowledge needed to excel in the field of machine learning. This course covers the practical algorithms for machine learning from a variety of perspectives. We cover topics such as FIND-S, Candidate Elimination Algorithm, Decision Tree (ID3 Algorithm), Backpropagation Algorithm, Naïve Bayesian Classifier, Bayesian Network, k-Means Algorithm, k-Nearest Neighbour Algorithm, Locally Weighted Regression Algorithm and Support Vector Machine Algorithm
COURSE OBJECTIVES
The student will be able to:
The student will be able to:
- Explain the concept learning algorithms
- Illustrate the supervised and unsupervised learning techniques
- Explain clustering and classification algorithms for predictions and evaluating hypotheses
COURSE OUTCOMES
- Describe the role and significance of various supervised, unsupervised, and reinforcement machine learning techniques in solving real-world problems.
- Analyze and illustrate the fundamental principles underlying concept learning algorithms, including the Find-S algorithm and the Candidate Elimination algorithm.
- Apply and examine various supervised learning techniques such as Decision trees, Artificial Neural Networks (ANN), Naive Bayes, K-Nearest Neighbors (KNN), and Bayesian belief networks
- Apply unsupervised learning techniques such as K-means clustering and hierarchical clustering to make predictions and validate hypotheses in diverse datasets.
- Analyze the concepts of regression and classification algorithm techniques like the non-parametric locally weighted regression algorithm and support vector machine.
List of problems for which students should develop programs and execute in the Laboratory
1. Aim: Illustrate and Demonstrate the working model and principles of the Find-S algorithm
Program: For a given set of training data examples stored in a CSV file, implement and demonstrate the Find-S algorithm to output a description of the set of all hypotheses consistent with the training examples.
Program: For a given set of training data examples stored in a CSV file, implement and demonstrate the Find-S algorithm to output a description of the set of all hypotheses consistent with the training examples.
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2. Aim: Demonstrate the working model and principles of the candidate elimination algorithm.
Program: For a given set of training data examples stored in a CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
Program: For a given set of training data examples stored in a CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
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3. Aim: To employ supervised learning principles to build a decision tree model that effectively learns from the training dataset.
Program: Write a program to demonstrate the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree, and apply this knowledge to classify a new sample.
Program: Write a program to demonstrate the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree, and apply this knowledge to classify a new sample.
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