artificial intelligence and cognitive modeling
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Artificial Intelligence and Cognitive Modeling. Laboratory for Cognitive Modeling 4.11.2011. lkm.fri.uni -lj.si. Terminology, terminology…. Artificial Intelligence. Machine Learning. Data Mining. Cognitive Modeling. lkm.fri.uni-lj.si. Data model. Data modeling. - PowerPoint PPT PresentationTRANSCRIPT
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Artificial Intelligence and Cognitive Modeling
Laboratory for Cognitive Modeling
4.11.2011
lkm.fri.uni-lj.si
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Terminology, terminology…
Artificial Intelligence Machine Learning
Data Mining
Cognitive Modeling
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Data modeling
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Cat. % nBad 52.01 168
Good 47.99 155Total (100.00) 323
Credit ranking (1=default)
Cat. % nBad 86.67 143
Good 13.33 22Total (51.08) 165
Paid Weekly/MonthlyP-value=0.0000, Chi-square=179.6665, df=1
Weekly pay
Cat. % nBad 15.82 25Good 84.18 133Total (48.92) 158
Monthly salary
Cat. % nBad 90.51 143
Good 9.49 15Total (48.92) 158
Age CategoricalP-value=0.0000, Chi-square=30.1113, df=1
Young (< 25);Middle (25-35)
Cat. % nBad 0.00 0Good 100.00 7Total (2.17) 7
Old ( > 35)
Cat. % nBad 48.98 24Good 51.02 25Total (15.17) 49
Age CategoricalP-value=0.0000, Chi-square=58.7255, df=1
Young (< 25)
Cat. % nBad 0.92 1Good 99.08 108Total (33.75) 109
Middle (25-35);Old ( > 35)
Cat. % nBad 0.00 0Good 100.00 8Total (2.48) 8
Social ClassP-value=0.0016, Chi-square=12.0388, df=1
Management;Clerical
Cat. % nBad 58.54 24
Good 41.46 17Total (12.69) 41
Professional
Data model
Different types of datafrom different sources
Data mining
Background knowledge
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Models and their use
• Supervised and unsupervised modeling
• Model types:– decision trees and
decision rules– artificial neural networks– regression trees– nearest neighbors– association rules– random forests– …
• Different models, different use:– model structure
(presentation of the relationship between inputs and outputs)
– prediction– associations (relationships)
between input values– clustering– outlier detection– …
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Example:applications in medical diagnostics and prognostics
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• modeling the knowledge and skills of specialist physicians
• using models for decision support
• scintigraphy of the skeleton and heart, oncology, traumatology, …
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Medical diagnostics and prognostics
• Input: background knowledge, descriptions of patients with subsequently confirmed diagnosis
• How to diagnose?• How to predict the occurrence of a disease
or its recurrence?• Very good results in specialized areas
(significantly better than specialists).
• What characteristics have the greatest impact on the disease?
• What is the reliability of computer predictions (diagnosis and prognosis)?
• How to explain predictions and bring them closer to doctors?
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Reliability estimation for medical diagnosis
General methods for estimating the reliability of individual predictions are developed.
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Diagnosis explanation
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General methods for explaining predictions are developed.
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Skeletal pathology detection
• Skeletal scintigraphy
• Background knowledge of human anatomy
• Known diagnoses
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Diagnosis of coronary artery disease
• Heart scintigraphy• Input data in the
form of images• Medical records• Reliability estimates
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Marketing
• How do customers decide what products to buy?
• How to arrange ads in an optimal way?
• When is the best time to broadcast television ads?
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Advanced sports analysis
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• in collaboration with the Faculty of Sport in Ljubljana :– analysis of the impact of rules
changes in 2010/11 season
• A basketball match simulation
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And more...
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• prediction market
• prediction intervals
• clickstream analysis
• façade analysis
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• Versatile applicability of artificial intelligence methods, especially data mining– ability to process large amounts of data– variety of data types– inclusion of background knowledge
• However: Artificial Intelligence (still) is not intelligence
Conclusion
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Scientific and developmental competence
We are the authors of numerous papers in scientific journals and books
(over 700 citations)
We are the authors of numerous papers in scientific journals and books
(over 700 citations)
We regularly participateat scientific conferences
and present our work
We regularly participateat scientific conferences
and present our work
We are members of editorial boards and program committees
We are members of editorial boards and program committees
We have a long experiencein the field of medicine, marketing,
financial sector, telecommunications ...
We have a long experiencein the field of medicine, marketing,
financial sector, telecommunications ...
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Institute of Oncology AD Consulting Bion Institute Jožef Stefan Institute-department of knowledge technologiesStarcom The Laboratory of NeuroendocrinologyClinic for Nuclear Medicine Intensio Faculty of sports
ASCR Institute of Computer Science
University of MalagaUniversity of Ioannina
University of Hasselt
Collaboration with other institutions
University of Porto
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University of Kragujevac
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Who are we?
prof. dr. Igor Kononenko
izr. prof. dr. Marko Robnik Šikonjadoc. dr. Matjaž Kukar
doc. dr. Zoran Bosnić
dr. Erik Štrumbelj as. mag. Petar Vračar
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Darko Pevec
as. Matej PičulinMiha DroleDomen Košir