Introduction to Programming - Stanford University

Introduction to Programming - Stanford University

Probabilistic Graphical Models Introduction Motivation

and Overview Daphne Koller Probabilistic Graphical Models Daphne Koller

Daphne Koller Models Daphne Koller

Uncertainty Partial knowledge of state of the world Noisy observations Phenomena not covered by our model Inherent stochasticity Daphne Koller

Probability Theory Declarative representation with clear semantics Powerful reasoning patterns Established learning methods Daphne Koller

Complex Systems Daphne Koller Graphical Models Bayesian networks Difficulty

Grade Letter Markov networks A Intelligence

SAT D B C

Daphne Koller Graphical Models Daphne Koller Graphical Models

Graphical representation: intuitive & compact data structure efficient reasoning using general algorithms can be learned from limited data Daphne Koller

Many Applications Medical diagnosis Computer vision Image segmentation Fault diagnosis 3D reconstruction

Natural language Holistic scene analysis processing Speech recognition Traffic analysis Social network models Robot localization & mapping

Message decoding Daphne Koller END END END Daphne Koller Suppose q is at a local minimum of a

function. What will one iteration of gradient descent do? Leave q unchanged. Change q in a random direction. Move q towards the global minimum of J(q). Decrease q.

Consider the weight update: Which of these is a correct vectorized implementation? Fig. A corresponds to a=0.01, Fig. B to a=0.1, Fig. C to a=1. Fig. A corresponds to a=0.1, Fig. B to a=0.01, Fig. C to a=1. Fig. A corresponds to a=1, Fig. B to a=0.01, Fig. C to a=0.1. Fig. A corresponds to a=1, Fig. B to a=0.1, Fig. C to a=0.01.

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