1. Motivation and Overview
Probabilistic
Graphical
Models
Introduction
Motivation and Overview

2. millions of pixels or
thousands of superpixels
predisposing factors
symptoms
test results
diseases
treatment outcomes
each needs to be labeled
{grass, sky, water, cow, horse, …}

3. Probabilistic
Graphical
Models

4. Models Data Model domain expert Declarative representation Learning
elicitation
Algorithm
Algorithm
Algorithm

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

6. Probability Theory Declarative representation with clear semantics
Powerful reasoning patterns
Established learning methods
*** Reasoning with probabilities
*** Well understood and useful for modeling uncertainty: dealing with noise, for modeling phenomenon not in our model, and uncertainty inherent in the system
*** We can learn probabilities from sets of observations

7. Complex Systems Random variables X1,…, Xn
predisposing factors
symptoms
test results
diseases
treatment outcomes
class labels for
thousands of superpixels
Random variables X1,…, Xn
Joint distribution P(X1,…, Xn)

8. Graphical Models Bayesian networks Markov networks Intelligence
Difficulty
Grade
Letter
SAT B D C A

9. Graphical Models

10. Graphical Representation
Intuitive & compact data structure
Efficient reasoning using general-purpose algorithms
Sparse parameterization
feasible elicitation
learning from data

11. Many Applications Medical diagnosis Computer vision 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

12. Image Segmentation

13. Medical Diagnosis -

14. Textual Information Extraction
Mrs.
Green
spoke
today in
New
York.
Green
chairs
the
finance
committee.
Person
Location
Person

15. Multi-Sensor Integration: Traffic
Trained on historical data
Learn to predict current & future road speed, including on unmeasured roads
Dynamic route optimization
Multiple views
on traffic
Weather
Learned
Model
Incident reports
I95 corridor experiment: accurate to 5 MPH in 85% of cases
Fielded in 72 cities
So far, we have focused on perception from a single source. There are also many cases where we have data streaming in from multiple sensors and information sources, often very heterogeneous in nature. Machine learning provides us with the tools to integrate those different sensors into a coherent picture. For example, a recently fielded traffic system.
Thanks to: Eric Horvitz, Microsoft Research

16. Speech Recognition K O N F I D E N S
[Source:

17. Biological Network Reconstruction1 2 3
CD3
CD28
LFA-1 L A T
PI3K
RAS
Cytohesin
JAB-1
Zap70
VAV
SLP-76 10 6 4
Lck
PKC
PLCg
PIP3
Akt 7 5
PIP2
PKA
Raf 8
MAPKKK
MAPKKK
Mek1/2 9
MEK4/7
MEK3/6
Erk1/2
Causal protein-signaling networks derived
from multiparameter single-cell data
Sachs et al., Science 2005
JNK
p38

18. Overview Representation Inference Learning Directed and undirected
Temporal models
More compact representations
Inference
Exact
Approximate
Learning
Parameters and structure
With and without complete data