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Mastering Machine Learning Algorithms
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Table of Contents

Table of Contents

  1. Machine Learning Model Fundamentals
  2. Introduction to Semi-Supervised Learning
  3. Graph-based Semi-Supervised Learning
  4. Bayesian Networks and Hidden Markov Models
  5. EM algorithm and applications
  6. Hebbian Learning
  7. Advanced Clustering and Feature Extraction
  8. Ensemble Learning
  9. Neural Networks for Machine Learning
  10. Advanced Neural Models
  11. Auto-Encoders
  12. Generative Adversarial Networks
  13. Deep Belief Networks
  14. Introduction to Reinforcement Learning
  15. Policy estimation algorithms

About the Author

Giuseppe Bonaccorso is an experienced team leader/manager in Artificial Intelligence and Machine/Deep Learning solution design, management, and delivery. He got his M.Sc.Eng. in Electronics Engineering in 2005 from University of Catania, Italy and continued his studies at the University of Rome Tor Vergata, Italy and the University of Essex, UK. His main interests include Machine/Deep Learning, Reinforcement Learning, bio-inspired adaptive systems, and Neural Language Processing.

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