ml 94 1 chap1 introduction
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)01-805-11-13(
http://faculties.sbu.ac.ir/~a_mahmoudi/
Machine Learning
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http://faculties.sbu.ac.ir/~a_mahmoudi/ML_94_1.htm
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Introduction to Machine Learning,
Third EditionEthem Alpaydin
Machine Learning: A Probabilistic
PrespectiveKevin Murphy
Pattern RecognitionTheodoridis & Koutroumbas
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Pattern Recognition and Machine Learning
Christopher Bishop
Pattern classificationRichard O. Duda, Peter E. Hart and David G. Stork
Machine Learning
Tom Mitchell
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25-2030-15
60-50
5%
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))Matlab
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1 Introduction2 Supervised learning
3 Bayesian Decision Theory
4 Parametric Methods
5 Multivariate Methods
6 Dimensionality Reduction
7 Nonparametric method
8 Decision Tree9 Linear Discrimination
10 Support Vector Machine
11 Neural Networks
12 Hidden Markov Model
13 Assessing Classification Algorithm
14 Combining Multiple Learner
15 Reinforcement Learning
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UCIRepository:
http://www.ics.uci.edu/~mlearn/MLRepository.html
UCIKDDArchive:
http://kdd.ics.uci.edu/summary.data.application.html
Statlib:http://lib.stat.cmu.edu/
Delve:http://www.cs.utoronto.ca/~delve/
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Journalof
Machine
Learning
Research
www.jmlr.org
MachineLearning
NeuralComputation
NeuralNetworks
IEEETransactionsonNeuralNetworks
IEEE
Transactions
on
Pattern
Analysis
and
Machine
Intelligence
AnnalsofStatistics
Journal
of
the
American
Statistical
Association PatternRecognition
Nature
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InternationalConference
on
Machine
Learning
(ICML) EuropeanConferenceonMachineLearning(ECML) NeuralInformationProcessingSystems(NIPS) Uncertainty
in
Artificial
Intelligence
(UAI)
ComputationalLearningTheory(COLT) InternationalConferenceonArtificialNeural
Networks(ICANN)
InternationalConferenceonAI&Statistics(AISTATS)
InternationalConferenceonPatternRecognition
(ICPR) ...
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Ethem Alpayd in
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Learning is the act of acquiring new, or modifying andreinforcing existing knowledge, behaviors, skills,
values, or preferences.
The ability to learn is possessed by humans, animalsand some machines.
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.).)Tom.M.Mitchell
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Machine learning is programming computers tooptimize a performance criterion using example dataor past experience.
Machine Learning
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Field of study that gives computers the ability
to learn without being explicitly programmed.
Well-posed Learning Problem: A computer program is said to learn from
experience E with respect to some task T and some performancemeasure P, if its performance on T, as measured by P, improves with
experience E.
Machine Learning
Arthur Samuel (1959)
Tom Mitchell (1998)
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USC CS Distinguished Lecture Series, 2008
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We are drowning in information and starving for
knowledge. John Naisbitt.
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online
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))CRM:
:
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))intrusion detection:
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Data Mining
Knowledge Discovery in Database (KDD)
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X .Y
P
(Y
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P(chips|beer)=0.7
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Learning Associations
Association Rule
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)creditscoring(
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Classifications
Discriminant:IFincome>1ANDsavings>2
THENlowrisk
ELSEhigh
risk
Discriminant
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(...
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))OCR
t?e
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Training examples of a person
Test images
ORL dataset,AT&T Laboratories, Cambridge UK
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(...
Sensorfusion
))outlierdetection
IntrusionDetectionSystems
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)supervised(
.
:
x
:carattributes
y
:
pricey=g(x| )
g
(
)
model, parameters
y
=wx+w0
y
=w2x2
+w1x+w0
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Response surface design
From Live Image quality database
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Supervised learning
Unsupervised learning
Reinforcement learning
Semi-supervised learning
Active learning
i d L i
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supervised Learning
U i d L i
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)regularity(
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):)clustering
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)Learningmotifs(
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Unsupervised Learning
Density estimation
)BSS(
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)BSS(
Microphone #1
Microphone #2
Speaker #1
Speaker #2
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Adopted from Dr. Andrew NG
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Image Segmentation
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(...
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Organizecomputingclusters
Socialnetworkanalysis
Imagecredit:NASA/JPLCaltech/E.Churchwell (Univ.ofWisconsin,Madison)
AstronomicaldataanalysisMarket
segmentation
Adopted from Dr. Andrew NG
semi-supervised Learning
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semi-supervised Learning
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Reinforcement Learning
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Reinforcement Learning
Game playing
Robot in a maze
Multiple agents, partial observability, ...
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)Deeplearning(
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