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Machine Learning.pdf

L11.2-prob-models-em.pdf
Preview of Machine Learning
🔗 Source: users.soict.hust.edu.vn
📊 Size: 945 KB
📄 Pages: 20 pages
⬇️ Downloads: 176

Summary

The content covers machine learning and data mining, including unsupervised and supervised learning, probabilistic modeling, and expectation maximization. It discusses difficulties such as no closed-form solution, no explicit expression of the density/mass function, and intractable inference. The EM algorithm is introduced, and Gaussian Mixture Models (GMM) are revisited, with the goal of maximizing the log-likelihood function using MLE, but notes that a closed-form solution cannot be found, and naive gradient descent is still challenging.

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Description

The content covers machine learning and data mining, including unsupervised and supervised learning, probabilistic modeling, and expectation maximization.

Technical Information

  • File Format: PDF
  • File Size: 945 KB
  • Pages: 20
  • Language: EN
  • Total Downloads: 176
  • Last Updated: 3 days ago

Document Overview

This PDF document about Machine Learning provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Machine Learning.

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If you're interested in Machine Learning, you might also want to explore:

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