Book Content
chapters • 19h12m total length
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
