Everyone:

Materials¶

Markov Models¶

1. Foundations of Computational Agents

   • Sections 9.6 (Reading)

2. Additional Resources

   • Markov Babbler (Notebook)
   • Harvard CS50 (Video)
   • Stanford CS221 (Video)
   • PML - Sections 3.6.1.2 (Reading)
   • N-Gram Language Models (Blog Post)
   • (1906) Extension of the Law of Large Numbers to Dependent Events (Markov) (Paper)
   • (1966) Statistical Inference for Probabilistic Functions of Finite State Markov Chains (Baum and Petrie) (Paper)
   • (1989) A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Rabiner) (Paper)

Bayesian Reasoning: Naive Bayes (Raschka) (Blog Post)¶

Additional Resources:

• Foundations of Computational Agents - Sections 9.1 through 9.4 (Reading)
• Probabilistic Machine Learning - Sections 2.3, 3.6.1, 4.6, 5.1, 9.3 (Reading)
• Introduction to Statistical Learning - Section 4.4.4 (Reading)
• Understanding Machine Learning - Chapter 24 (Reading)
• Harvard CS50 (Video)
• Stanford CS221 (Video)
• MIT 6.034 (Video)
• Naive Bayes for Iris Classification (Notebook)
• Naive Bayes Visualizer (Notebook)
• Cornell CS4780 (Video)
• Conditional Probability (StatQuest) (Video)
• Bayes' Theorem (StatQuest) (Video)
• Likelihood (StatQuest) (Video)
• PDFs (StatQuest) (Video)
• MLE (StatQuest) (Video)
• Naive Bayes (StatQuest) (Video)
• Gaussian NB (StatQuest) (Video)
• Bayesian Reasoning (HdM) (Blog Post)
• (1763) An Essay towards Solving a Problem in the Doctrine of Chances (Bayes) (Paper)
• (1812) Th\xE9orie Analytique des Probabilit\xE9s (Laplace) (Paper)
• (1985) Bayesian Networks - A Model of Self-Activated Memory for Evidential Reasoning (Pearl) (Paper)
• (1988) Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference (Pearl) (Paper)
• (1998) A Comparison of Event Models for Naive Bayes Text Classification (McCallum and Nigam) (Paper)