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Machine Learning, or, An Unofficial Guide to Georgia Institute of Technology's CS7641: Machine Learning by George Kudrayvtsev is a nonfiction available to read on EtoBox.

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Contents I Supervised Learning Techniques Classification Decision Trees Getting Answers Asking Questions: The ID3 Algorithm Considerations Ensemble Learning Bagging Boosting Support Vector Machines There are lines and there are lines... Support Vectors Extending SVMs: The Kernel Trick Summary Regression Linear Regression Neural Networks Perceptron Sigmoids Structure Biases Instance-Based Learning Nearest Neighbors Computational Learning Theory Learning to Learn: Interactions Space Complexity Version Spaces Error PAC Learning Epsilon Exhaustion Infinite Hypothesis Spaces Intuition Vapnik-Chervonenkis Dimension Information Theory Entropy: Information Certainty Joint Entropy: Mutual Information Kullback-Leibler Divergence Bayesian Learning Bayesian Learning Finding the Best Hypothesis Finding the Best Label Bayesian Inference Bayesian Networks Making Inferences Naïve Bayes II Unsupervised Learning Randomized Optimization Hill Climbing Simulated Annealing Genetic Algorithms High-Level Algorithm Cross-Over Challenges MIMIC High-Level Algorithm Estimating Distributions Practical Considerations Clustering Single Linkage Clustering Considerations k-Means Clustering Convergence Consideratio

Who reads Machine Learning, or, An Unofficial Guide to Georgia Institute of Technology's CS7641: Machine Learning?

Author

George Kudrayvtsev

Published

2020

Language

EN

Category

nonfiction

Subjects

Computer Science, Technology, Stem