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Chapter 20: Learning Probabilistic Models
ARTIFICIAL INTELLIGENCE MODERN APPROACH
Visual Summaries
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Table Summary
Core Terminology
Master key academic terminology through active recall and spaced repetition concepts.
Maximum A Posteriori (MAP) hypothesis.
The hypothesis hMAP​ that maximizes the posterior probability P(hi​∣d).
density estimation
The general task of learning a probability model from data assumed to be generated by that model.
The L2​ lossÂ
In a linear Gaussian model, maximizing log likelihood is equivalent to minimizing which error metric?
The beta distribution family
Which distribution family serves as the conjugate prior for a Boolean variable?
Expectation-Maximization
What does the acronym EM stand for in the context of learning with hidden variables?
Squared error
Which specific loss function is minimized when finding the ML estimate of a linear Gaussian model?
Chapter Quiz
According to the source, which capability is uniquely required for a computer to pass the 'Total Turing Test' compared to the standard Turing Test?
What is a primary obstacle to the 'laws of thought' (logicist) approach to artificial intelligence?
Which field is described as combining probability theory with utility theory to provide a framework for decisions made under uncertainty?
The 'General Problem Solver' (GPS) was the first program designed to embody which specific approach to AI?
Why was the DENDRAL program significant in the history of AI development?
Answers: D, C, A, D, A
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