Motivation and Overview

AI and Robotics

Nov 7, 2013 (4 years and 8 months ago)

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Daphne Koller

Introduction

Motivation
and Overview

Probabilistic

Graphical

Models

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

E
stablished learning methods

Daphne Koller

Complex Systems

Daphne Koller

Graphical Models

Intelligence

Difficulty

Letter

SAT

B

D

C

A

Bayesian networks

Markov networks

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

Fault diagnosis

Natural language
processing

Traffic analysis

Social network models

Message decoding

Computer vision

Image segmentation

3D reconstruction

Holistic scene analysis

Speech recognition

Robot localization &
mapping

Daphne Koller

END END END

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Suppose
q

is at a local minimum of a
function. What will one iteration of

Leave
q

unchanged.

Change
q

in a random direction.

Move
q

towards the global minimum of J(
q
).

Decrease
q
.

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Consider the weight update:

Which of these is a correct vectorized implementation?

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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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Ordering of

buttons is:

13

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