SlideShare a Scribd company logo
1 of 36
Introduction to Statistical Model Selection Huimin Chen Department of Electrical Engineering University of New Orleans New Orleans, LA 70148
Typical Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Modeling and  Model Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline of This Talk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Regression Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Empirical Risk Functional ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How to measure the model complexity with finite data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Risk ,[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Risk (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Risk (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Risk (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Model Selection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Model Selection (Cont’d) ,[object Object],[object Object],[object Object]
Bayesian Model Selection (Cont’d) ,[object Object],[object Object],Define so  that Use quadratic approximation Log-likelihood ≈ −
Bayesian Model Selection (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Theoretic Measures ,[object Object],[object Object],[object Object],[object Object]
Information Theoretic Measures (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Theoretic Measures (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle ,[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MDL Principle (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical Regularization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical Regularization (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Adaptive Methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
Adaptive Methods (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
From model selection to model evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
From model selection to model evaluation (Cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future Research Directions ,[object Object],[object Object],[object Object],[object Object]
Further Readings ,[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Training Neural Networks
Training Neural NetworksTraining Neural Networks
Training Neural NetworksDatabricks
 
Hierarchical clustering.pptx
Hierarchical clustering.pptxHierarchical clustering.pptx
Hierarchical clustering.pptxNTUConcepts1
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnBenjamin Bengfort
 
Bayes Classification
Bayes ClassificationBayes Classification
Bayes Classificationsathish sak
 
Lecture 9 Markov decision process
Lecture 9 Markov decision processLecture 9 Markov decision process
Lecture 9 Markov decision processVARUN KUMAR
 
Artificial Neural Networks - ANN
Artificial Neural Networks - ANNArtificial Neural Networks - ANN
Artificial Neural Networks - ANNMohamed Talaat
 
Simple overview of machine learning
Simple overview of machine learningSimple overview of machine learning
Simple overview of machine learningpriyadharshini R
 
backpropagation in neural networks
backpropagation in neural networksbackpropagation in neural networks
backpropagation in neural networksAkash Goel
 
Instance based learning
Instance based learningInstance based learning
Instance based learningSlideshare
 
Supervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And TechniquesSupervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And TechniquesSlideTeam
 
K-Folds Cross Validation Method
K-Folds Cross Validation MethodK-Folds Cross Validation Method
K-Folds Cross Validation MethodSHUBHAM GUPTA
 
Deep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and RegularizationDeep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and RegularizationYan Xu
 
Perceptron (neural network)
Perceptron (neural network)Perceptron (neural network)
Perceptron (neural network)EdutechLearners
 
Relational knowledge distillation
Relational knowledge distillationRelational knowledge distillation
Relational knowledge distillationNAVER Engineering
 

What's hot (20)

Training Neural Networks
Training Neural NetworksTraining Neural Networks
Training Neural Networks
 
Hierarchical clustering.pptx
Hierarchical clustering.pptxHierarchical clustering.pptx
Hierarchical clustering.pptx
 
K Nearest Neighbors
K Nearest NeighborsK Nearest Neighbors
K Nearest Neighbors
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-Learn
 
Bayes Classification
Bayes ClassificationBayes Classification
Bayes Classification
 
Lecture 9 Markov decision process
Lecture 9 Markov decision processLecture 9 Markov decision process
Lecture 9 Markov decision process
 
Artificial Neural Networks - ANN
Artificial Neural Networks - ANNArtificial Neural Networks - ANN
Artificial Neural Networks - ANN
 
Simple overview of machine learning
Simple overview of machine learningSimple overview of machine learning
Simple overview of machine learning
 
Presentation on K-Means Clustering
Presentation on K-Means ClusteringPresentation on K-Means Clustering
Presentation on K-Means Clustering
 
backpropagation in neural networks
backpropagation in neural networksbackpropagation in neural networks
backpropagation in neural networks
 
Clustering
ClusteringClustering
Clustering
 
Instance based learning
Instance based learningInstance based learning
Instance based learning
 
K - Nearest neighbor ( KNN )
K - Nearest neighbor  ( KNN )K - Nearest neighbor  ( KNN )
K - Nearest neighbor ( KNN )
 
Supervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And TechniquesSupervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And Techniques
 
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Support Vector Machines ( SVM )
Support Vector Machines ( SVM )
 
K-Folds Cross Validation Method
K-Folds Cross Validation MethodK-Folds Cross Validation Method
K-Folds Cross Validation Method
 
Bayesian network
Bayesian networkBayesian network
Bayesian network
 
Deep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and RegularizationDeep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and Regularization
 
Perceptron (neural network)
Perceptron (neural network)Perceptron (neural network)
Perceptron (neural network)
 
Relational knowledge distillation
Relational knowledge distillationRelational knowledge distillation
Relational knowledge distillation
 

Viewers also liked

IMAG 4850 Library Presentation
IMAG 4850 Library PresentationIMAG 4850 Library Presentation
IMAG 4850 Library Presentationedward.eckel
 
Presentation Of My Skills
Presentation  Of  My SkillsPresentation  Of  My Skills
Presentation Of My SkillsMfoghama
 
Emerging Voices for Global Health
Emerging Voices for Global HealthEmerging Voices for Global Health
Emerging Voices for Global HealthDavid Hercot
 
Single Electron Spin Detection Slides For Uno Interview
Single Electron Spin Detection Slides For Uno InterviewSingle Electron Spin Detection Slides For Uno Interview
Single Electron Spin Detection Slides For Uno Interviewchenhm
 
AfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LICAfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LICDavid Hercot
 
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...edward.eckel
 
Removing User Fees In SSA D Hercot
Removing User Fees In SSA D HercotRemoving User Fees In SSA D Hercot
Removing User Fees In SSA D HercotDavid Hercot
 
Sports Programs
Sports ProgramsSports Programs
Sports Programsdconradt
 
Hercot How to do a policy delphi
Hercot How to do a policy delphiHercot How to do a policy delphi
Hercot How to do a policy delphiDavid Hercot
 
Sinagoga Neologa din Cluj-Napoca
Sinagoga Neologa din Cluj-NapocaSinagoga Neologa din Cluj-Napoca
Sinagoga Neologa din Cluj-NapocaBako Gabor
 
Parcul Naţional Retezat
Parcul Naţional RetezatParcul Naţional Retezat
Parcul Naţional RetezatBako Gabor
 
Intro to Feature Selection
Intro to Feature SelectionIntro to Feature Selection
Intro to Feature Selectionchenhm
 
Intro to Multitarget Tracking for CURVE
Intro to Multitarget Tracking for CURVEIntro to Multitarget Tracking for CURVE
Intro to Multitarget Tracking for CURVEchenhm
 

Viewers also liked (15)

IMAG 4850 Library Presentation
IMAG 4850 Library PresentationIMAG 4850 Library Presentation
IMAG 4850 Library Presentation
 
Presentation Of My Skills
Presentation  Of  My SkillsPresentation  Of  My Skills
Presentation Of My Skills
 
Bilva
BilvaBilva
Bilva
 
Emerging Voices for Global Health
Emerging Voices for Global HealthEmerging Voices for Global Health
Emerging Voices for Global Health
 
Single Electron Spin Detection Slides For Uno Interview
Single Electron Spin Detection Slides For Uno InterviewSingle Electron Spin Detection Slides For Uno Interview
Single Electron Spin Detection Slides For Uno Interview
 
AfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LICAfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LIC
 
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
 
Removing User Fees In SSA D Hercot
Removing User Fees In SSA D HercotRemoving User Fees In SSA D Hercot
Removing User Fees In SSA D Hercot
 
S I D A
S I D AS I D A
S I D A
 
Sports Programs
Sports ProgramsSports Programs
Sports Programs
 
Hercot How to do a policy delphi
Hercot How to do a policy delphiHercot How to do a policy delphi
Hercot How to do a policy delphi
 
Sinagoga Neologa din Cluj-Napoca
Sinagoga Neologa din Cluj-NapocaSinagoga Neologa din Cluj-Napoca
Sinagoga Neologa din Cluj-Napoca
 
Parcul Naţional Retezat
Parcul Naţional RetezatParcul Naţional Retezat
Parcul Naţional Retezat
 
Intro to Feature Selection
Intro to Feature SelectionIntro to Feature Selection
Intro to Feature Selection
 
Intro to Multitarget Tracking for CURVE
Intro to Multitarget Tracking for CURVEIntro to Multitarget Tracking for CURVE
Intro to Multitarget Tracking for CURVE
 

Similar to Intro to Model Selection

Download It
Download ItDownload It
Download Itbutest
 
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional ExpertsProbability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional ExpertsChirag Gupta
 
ProbabilisticModeling20080411
ProbabilisticModeling20080411ProbabilisticModeling20080411
ProbabilisticModeling20080411Clay Stanek
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.pptbutest
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.pptbutest
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.pptbutest
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401butest
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401butest
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401butest
 
. An introduction to machine learning and probabilistic ...
. An introduction to machine learning and probabilistic .... An introduction to machine learning and probabilistic ...
. An introduction to machine learning and probabilistic ...butest
 
Model-driven decision support for monitoring network design based on analysis...
Model-driven decision support for monitoring network design based on analysis...Model-driven decision support for monitoring network design based on analysis...
Model-driven decision support for monitoring network design based on analysis...Velimir (monty) Vesselinov
 
November, 2006 CCKM'06 1
November, 2006 CCKM'06 1 November, 2006 CCKM'06 1
November, 2006 CCKM'06 1 butest
 
slides
slidesslides
slidesbutest
 
original
originaloriginal
originalbutest
 
Presentation on Machine Learning and Data Mining
Presentation on Machine Learning and Data MiningPresentation on Machine Learning and Data Mining
Presentation on Machine Learning and Data Miningbutest
 
A General Framework for Accurate and Fast Regression by Data Summarization in...
A General Framework for Accurate and Fast Regression by Data Summarization in...A General Framework for Accurate and Fast Regression by Data Summarization in...
A General Framework for Accurate and Fast Regression by Data Summarization in...Yao Wu
 
Eswc2009
Eswc2009Eswc2009
Eswc2009fanizzi
 
Jörg Stelzer
Jörg StelzerJörg Stelzer
Jörg Stelzerbutest
 

Similar to Intro to Model Selection (20)

Download It
Download ItDownload It
Download It
 
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional ExpertsProbability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
 
ProbabilisticModeling20080411
ProbabilisticModeling20080411ProbabilisticModeling20080411
ProbabilisticModeling20080411
 
Into to prob_prog_hari
Into to prob_prog_hariInto to prob_prog_hari
Into to prob_prog_hari
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.ppt
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.ppt
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.ppt
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401
 
Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401Machine Learning: Foundations Course Number 0368403401
Machine Learning: Foundations Course Number 0368403401
 
. An introduction to machine learning and probabilistic ...
. An introduction to machine learning and probabilistic .... An introduction to machine learning and probabilistic ...
. An introduction to machine learning and probabilistic ...
 
Model-driven decision support for monitoring network design based on analysis...
Model-driven decision support for monitoring network design based on analysis...Model-driven decision support for monitoring network design based on analysis...
Model-driven decision support for monitoring network design based on analysis...
 
November, 2006 CCKM'06 1
November, 2006 CCKM'06 1 November, 2006 CCKM'06 1
November, 2006 CCKM'06 1
 
slides
slidesslides
slides
 
original
originaloriginal
original
 
Presentation on Machine Learning and Data Mining
Presentation on Machine Learning and Data MiningPresentation on Machine Learning and Data Mining
Presentation on Machine Learning and Data Mining
 
A General Framework for Accurate and Fast Regression by Data Summarization in...
A General Framework for Accurate and Fast Regression by Data Summarization in...A General Framework for Accurate and Fast Regression by Data Summarization in...
A General Framework for Accurate and Fast Regression by Data Summarization in...
 
CLIM Program: Remote Sensing Workshop, Foundations Session: A Discussion - Br...
CLIM Program: Remote Sensing Workshop, Foundations Session: A Discussion - Br...CLIM Program: Remote Sensing Workshop, Foundations Session: A Discussion - Br...
CLIM Program: Remote Sensing Workshop, Foundations Session: A Discussion - Br...
 
Eswc2009
Eswc2009Eswc2009
Eswc2009
 
Jörg Stelzer
Jörg StelzerJörg Stelzer
Jörg Stelzer
 

Recently uploaded

H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 

Recently uploaded (20)

H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 

Intro to Model Selection

  • 1. Introduction to Statistical Model Selection Huimin Chen Department of Electrical Engineering University of New Orleans New Orleans, LA 70148
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.