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techguideReading Harvard CS50 Introduction to Artificial Intelligence with Python

Uncertainty — Probability, Bayesian Networks, Markov Models & Genetic Inference | Harvard CS50 AI Week 2

Week 2 is about probability: conditional probability and Bayes' rule turn evidence into probabilities; a Bayesian network uses arrows to split a joint probability into one conditional probability table per node, inference by enumeration is exact but slow, and rejection sampling and likelihood weighting approximate it with samples; a Markov chain describes a state that changes over time with a transition model, and a hidden Markov model adds a sensor model to work back from observations to states you cannot see. With time links for every section, plus the approach and checks for the Heredity and PageRank projects.

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aideep-diveReading Stanford CS124 From Languages to Information

PageRank and Social Networks: From Anchor Text and Centrality to the Course Wrap-Up | Stanford CS124 Week 10

Week 10 treats web pages and human relationships as graphs. Link analysis teaches anchor text and PageRank: the long-run probability that someone clicking links at random is on a page is that page's score. Social networks covers centrality, the clustering coefficient, small worlds and weak ties. The final live lecture returns to LLMs with preference alignment, interpretability and machine translation.

#cs124#stanford#pagerank#social-network#graph