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This document is a comprehensive collection of Machine Learning questions and answers, designed for exam preparation. It covers a wide range of topics from the fundamentals of Machine Learning to advanced algorithms, providing clear explanations and solutions to commonly asked questions. The content includes practical problem-solving and in-depth answers to help students grasp key concepts effectively. Perfect for B.Tech students in Computer Science or related fields, this document will help you practice for exams like GATE, ESE, or university-level Machine Learning courses. Index of Topics: Basics of Machine Learning Supervised Learning Algorithms (e.g., Linear Regression, Decision Trees) Unsupervised Learning (e.g., K-Means, PCA) Neural Networks and Deep Learning Clustering and Classification Model Evaluation and Performance Metrics Frequently Asked Questions with Detailed Solutions Prepared By: SK Course: Machine Learning / Artificial Intelligence Format: PDF
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Probably Approximately Correct (PAC) Learning - Explained in Points
To be considered PAC, a learning algorithm must satisfy the following:
Hypothesis Class with Multiple Rectangles - Easy Explanation
3A) Bayesian Decision Theory Explained in Points
(i) Prior Probability:
(ii) Conditional Probability:
(iii) Posterior Probability:
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Support Calculation
Confidence Calculation
o Leads to overfitting , where the model performs well on training data but poorly on test data.
4B) Importance of Dimensionality Reduction in Machine Learning Importance of Dimensionality Reduction in Machine Learning (Points)