SlideShare a Scribd company logo

notes as .ppt

•Download as PPT, PDF•
•
B
butest
1 of 36
CSC2515 Fall 2007 Introduction to Machine Learning Lecture 1: What is Machine Learning? All lecture slides will be available as .ppt, .ps, & .htm at www.cs.toronto.edu/~hinton Many of the figures are provided by Chris Bishop from his textbook: ”Pattern Recognition and Machine Learning”
What is Machine Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A classic example of a task that requires machine learning: It is very hard to say what makes a 2
Some more examples of tasks that are best solved by using a learning algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some web-based examples of machine learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Displaying the structure of a set of documents using Latent Semantic Analysis (a form of PCA) Each document is converted to a vector of word counts. This vector is then mapped to two coordinates and displayed as a colored dot. The colors represent the hand-labeled classes. When the documents are laid out in 2-D, the classes are not used. So we can judge how good the algorithm is by seeing if the classes are separated.
Displaying the structure of a set of documents using a deep neural network
Machine Learning & Symbolic AI ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning & Statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A spectrum of machine learning tasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Statistics--- --- --- --- --- --- --- Artificial Intelligence
Types of learning task ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hypothesis Space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Searching a hypothesis space ,[object Object],[object Object],[object Object],[object Object]
Some Loss Functions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generalization ,[object Object],[object Object],[object Object],[object Object],[object Object]
Trading off the goodness of fit against the complexity of the model ,[object Object],[object Object],[object Object],[object Object]
A sampling assumption ,[object Object],[object Object],[object Object],[object Object],[object Object]
The probabilistic guarantee ,[object Object],[object Object],[object Object],[object Object],[object Object]
A simple example: Fitting a polynomial ,[object Object],[object Object],[object Object],from Bishop
Some fits to the data: which is best? from Bishop
A simple way to reduce model complexity ,[object Object],regularization parameter target value penalized loss function from Bishop
Regularization: vs.
Polynomial Coefficients
Using a validation set ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Bayesian framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A coin tossing example ,[object Object],[object Object],[object Object],probability of a particular sequence
Some problems with picking the parameters that are most likely to generate the data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using a distribution over parameter values ,[object Object],[object Object],[object Object],probability density p area=1 area=1 0 1 1 1 2 probability density probability density
Lets do it again: Suppose we get a tail ,[object Object],[object Object],[object Object],probability density p area=1 area=1 0 1 1 2
Lets do it another 98 times ,[object Object],probability density p area=1 0 1 1 2
Bayes Theorem Prior probability of weight vector W Posterior probability of weight vector W given training data D Probability of observed data given W joint probability conditional probability
A cheap trick to avoid computing the posterior probabilities of all weight vectors ,[object Object],[object Object],[object Object]
Why we maximize sums of log probs ,[object Object],[object Object],[object Object]
A even cheaper trick ,[object Object],[object Object],[object Object],[object Object]
Supervised Maximum Likelihood Learning ,[object Object],d = the correct answer y = model’s estimate of most probable value
Supervised Maximum Likelihood Learning ,[object Object],[object Object],[object Object]

Recommended

Recommendation system using collaborative deep learning
Recommendation system using collaborative deep learningRecommendation system using collaborative deep learning
Recommendation system using collaborative deep learningRitesh Sawant
 
Identification of Relevant Sections in Web Pages Using a Machine Learning App...
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Identification of Relevant Sections in Web Pages Using a Machine Learning App...
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Jerrin George
 
Machine Learning Real Life Applications By Examples
Machine Learning Real Life Applications By ExamplesMachine Learning Real Life Applications By Examples
Machine Learning Real Life Applications By ExamplesMario Cartia
 
Using Deep Learning to Find Similar Dresses
Using Deep Learning to Find Similar DressesUsing Deep Learning to Find Similar Dresses
Using Deep Learning to Find Similar DressesHJ van Veen
 
Cat and dog classification
Cat and dog classificationCat and dog classification
Cat and dog classificationomaraldabash
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningRahul Jain
 
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
 
Terminology Machine Learning
Terminology Machine LearningTerminology Machine Learning
Terminology Machine LearningDataminingTools Inc
 

Recommended

Recommendation system using collaborative deep learning
Recommendation system using collaborative deep learningRecommendation system using collaborative deep learning
Recommendation system using collaborative deep learningRitesh Sawant
 
Identification of Relevant Sections in Web Pages Using a Machine Learning App...
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Identification of Relevant Sections in Web Pages Using a Machine Learning App...
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Jerrin George
 
Machine Learning Real Life Applications By Examples
Machine Learning Real Life Applications By ExamplesMachine Learning Real Life Applications By Examples
Machine Learning Real Life Applications By ExamplesMario Cartia
 
Using Deep Learning to Find Similar Dresses
Using Deep Learning to Find Similar DressesUsing Deep Learning to Find Similar Dresses
Using Deep Learning to Find Similar DressesHJ van Veen
 
Cat and dog classification
Cat and dog classificationCat and dog classification
Cat and dog classificationomaraldabash
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningRahul Jain
 
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
 
Terminology Machine Learning
Terminology Machine LearningTerminology Machine Learning
Terminology Machine LearningDataminingTools Inc
 
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
 
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
 
Machine learning for_finance
Machine learning for_financeMachine learning for_finance
Machine learning for_financeStefan Duprey
 
Machine model to classify dogs and cat
Machine model to classify dogs and catMachine model to classify dogs and cat
Machine model to classify dogs and catAkash Parui
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningShahar Cohen
 
10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle CompetitionsDataRobot
 
ML DL AI DS BD - An Introduction
ML DL AI DS BD - An IntroductionML DL AI DS BD - An Introduction
ML DL AI DS BD - An IntroductionDony Riyanto
 
The Incredible Disappearing Data Scientist
The Incredible Disappearing Data ScientistThe Incredible Disappearing Data Scientist
The Incredible Disappearing Data ScientistRebecca Bilbro
 
Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)Oswald Campesato
 
Learning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learningLearning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learningSimon John
 
Introduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-LearnIntroduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
 
Machine Learning ICS 273A
Machine Learning ICS 273AMachine Learning ICS 273A
Machine Learning ICS 273Abutest
 
Model Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep LearningModel Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep LearningPramit Choudhary
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.butest
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)Benjamin Bengfort
 
Basic ideas on keras framework
Basic ideas on keras frameworkBasic ideas on keras framework
Basic ideas on keras frameworkAlison Marczewski
 
Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial Alexandros Karatzoglou
 
(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine Learning(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine LearningRebecca Bilbro
 
BigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for SparkBigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
 
Lecture 1
Lecture 1Lecture 1
Lecture 1Aun Akbar
 
lec1.ppt
lec1.pptlec1.ppt
lec1.pptSVasuKrishna1
 

More Related Content

What's hot

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
 
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
 
Machine learning for_finance
Machine learning for_financeMachine learning for_finance
Machine learning for_financeStefan Duprey
 
Machine model to classify dogs and cat
Machine model to classify dogs and catMachine model to classify dogs and cat
Machine model to classify dogs and catAkash Parui
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningShahar Cohen
 
10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle CompetitionsDataRobot
 
ML DL AI DS BD - An Introduction
ML DL AI DS BD - An IntroductionML DL AI DS BD - An Introduction
ML DL AI DS BD - An IntroductionDony Riyanto
 
The Incredible Disappearing Data Scientist
The Incredible Disappearing Data ScientistThe Incredible Disappearing Data Scientist
The Incredible Disappearing Data ScientistRebecca Bilbro
 
Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)Oswald Campesato
 
Learning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learningLearning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learningSimon John
 
Introduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-LearnIntroduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
 
Machine Learning ICS 273A
Machine Learning ICS 273AMachine Learning ICS 273A
Machine Learning ICS 273Abutest
 
Model Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep LearningModel Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep LearningPramit Choudhary
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.butest
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)Benjamin Bengfort
 
Basic ideas on keras framework
Basic ideas on keras frameworkBasic ideas on keras framework
Basic ideas on keras frameworkAlison Marczewski
 
Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial Alexandros Karatzoglou
 
(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine Learning(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine LearningRebecca Bilbro
 
BigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for SparkBigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
 

What's hot (20)

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
 
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
 
Machine learning for_finance
Machine learning for_financeMachine learning for_finance
Machine learning for_finance
 
Machine model to classify dogs and cat
Machine model to classify dogs and catMachine model to classify dogs and cat
Machine model to classify dogs and cat
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions
 
ML DL AI DS BD - An Introduction
ML DL AI DS BD - An IntroductionML DL AI DS BD - An Introduction
ML DL AI DS BD - An Introduction
 
The Incredible Disappearing Data Scientist
The Incredible Disappearing Data ScientistThe Incredible Disappearing Data Scientist
The Incredible Disappearing Data Scientist
 
Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)Diving into Deep Learning (Silicon Valley Code Camp 2017)
Diving into Deep Learning (Silicon Valley Code Camp 2017)
 
Learning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learningLearning to compare: relation network for few shot learning
Learning to compare: relation network for few shot learning
 
Introduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-LearnIntroduction to Machine Learning in Python using Scikit-Learn
Introduction to Machine Learning in Python using Scikit-Learn
 
Machine Learning ICS 273A
Machine Learning ICS 273AMachine Learning ICS 273A
Machine Learning ICS 273A
 
Model Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep LearningModel Evaluation in the land of Deep Learning
Model Evaluation in the land of Deep Learning
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
 
Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)Evolutionary Design of Swarms (SSCI 2014)
Evolutionary Design of Swarms (SSCI 2014)
 
Basic ideas on keras framework
Basic ideas on keras frameworkBasic ideas on keras framework
Basic ideas on keras framework
 
Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial
 
(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine Learning(Py)testing the Limits of Machine Learning
(Py)testing the Limits of Machine Learning
 
BigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for SparkBigDL webinar - Deep Learning Library for Spark
BigDL webinar - Deep Learning Library for Spark
 

Similar to notes as .ppt

Machine learning interview questions and answers
Machine learning interview questions and answersMachine learning interview questions and answers
Machine learning interview questions and answerskavinilavuG
 
Machine Learning basics
Machine Learning basicsMachine Learning basics
Machine Learning basicsNeeleEilers
 
Machine learning-in-details-with-out-python-code
Machine learning-in-details-with-out-python-codeMachine learning-in-details-with-out-python-code
Machine learning-in-details-with-out-python-codeOsama Ghandour Geris
 
ML crash course
ML crash courseML crash course
ML crash coursemikaelhuss
 
End-to-End Machine Learning Project
End-to-End Machine Learning ProjectEnd-to-End Machine Learning Project
End-to-End Machine Learning ProjectEng Teong Cheah
 
Deep learning Introduction and Basics
Deep learning Introduction and BasicsDeep learning Introduction and Basics
Deep learning Introduction and BasicsNitin Mishra
 
machinecanthink-160226155704.pdf
machinecanthink-160226155704.pdfmachinecanthink-160226155704.pdf
machinecanthink-160226155704.pdfPranavPatil822557
 
Mis End Term Exam Theory Concepts
Mis End Term Exam Theory ConceptsMis End Term Exam Theory Concepts
Mis End Term Exam Theory ConceptsVidya sagar Sharma
 
introduction to machine learning
introduction to machine learningintroduction to machine learning
introduction to machine learningJohnson Ubah
 
Overfitting & Underfitting
Overfitting & UnderfittingOverfitting & Underfitting
Overfitting & UnderfittingSOUMIT KAR
 
Summary.ppt
Summary.pptSummary.ppt
Summary.pptbutest
 
Statistical foundations of ml
Statistical foundations of mlStatistical foundations of ml
Statistical foundations of mlVipul Kalamkar
 
Getting started with Machine Learning
Getting started with Machine LearningGetting started with Machine Learning
Getting started with Machine LearningGaurav Bhalotia
 
A Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine LearningA Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine Learningnep_test_account
 

Similar to notes as .ppt (20)

Lecture 1
Lecture 1Lecture 1
Lecture 1
 
lec1.ppt
lec1.pptlec1.ppt
lec1.ppt
 
Machine learning interview questions and answers
Machine learning interview questions and answersMachine learning interview questions and answers
Machine learning interview questions and answers
 
Machine Learning basics
Machine Learning basicsMachine Learning basics
Machine Learning basics
 
Machine learning-in-details-with-out-python-code
Machine learning-in-details-with-out-python-codeMachine learning-in-details-with-out-python-code
Machine learning-in-details-with-out-python-code
 
ML crash course
ML crash courseML crash course
ML crash course
 
End-to-End Machine Learning Project
End-to-End Machine Learning ProjectEnd-to-End Machine Learning Project
End-to-End Machine Learning Project
 
Machine Learning.pptx
Machine Learning.pptxMachine Learning.pptx
Machine Learning.pptx
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
Deep learning Introduction and Basics
Deep learning Introduction and BasicsDeep learning Introduction and Basics
Deep learning Introduction and Basics
 
Machine Can Think
Machine Can ThinkMachine Can Think
Machine Can Think
 
machinecanthink-160226155704.pdf
machinecanthink-160226155704.pdfmachinecanthink-160226155704.pdf
machinecanthink-160226155704.pdf
 
Mis End Term Exam Theory Concepts
Mis End Term Exam Theory ConceptsMis End Term Exam Theory Concepts
Mis End Term Exam Theory Concepts
 
introduction to machine learning
introduction to machine learningintroduction to machine learning
introduction to machine learning
 
Overfitting & Underfitting
Overfitting & UnderfittingOverfitting & Underfitting
Overfitting & Underfitting
 
Summary.ppt
Summary.pptSummary.ppt
Summary.ppt
 
Statistical foundations of ml
Statistical foundations of mlStatistical foundations of ml
Statistical foundations of ml
 
Getting started with Machine Learning
Getting started with Machine LearningGetting started with Machine Learning
Getting started with Machine Learning
 
A Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine LearningA Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine Learning
 
PREDICT 422 - Module 1.pptx
PREDICT 422 - Module 1.pptxPREDICT 422 - Module 1.pptx
PREDICT 422 - Module 1.pptx
 

More from butest

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEbutest
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jacksonbutest
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer IIbutest
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.docbutest
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1butest
 
Facebook
Facebook Facebook
Facebook butest
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...butest
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTbutest
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docbutest
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docbutest
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.docbutest
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!butest
 

More from butest (20)

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer II
 
PPT
PPTPPT
PPT
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.doc
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
 
Facebook
Facebook Facebook
Facebook
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.doc
 
hier
hierhier
hier
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!
 

notes as .ppt

  • 1. CSC2515 Fall 2007 Introduction to Machine Learning Lecture 1: What is Machine Learning? All lecture slides will be available as .ppt, .ps, & .htm at www.cs.toronto.edu/~hinton Many of the figures are provided by Chris Bishop from his textbook: ”Pattern Recognition and Machine Learning”
  • 2.
  • 3. A classic example of a task that requires machine learning: It is very hard to say what makes a 2
  • 4.
  • 5.
  • 6. Displaying the structure of a set of documents using Latent Semantic Analysis (a form of PCA) Each document is converted to a vector of word counts. This vector is then mapped to two coordinates and displayed as a colored dot. The colors represent the hand-labeled classes. When the documents are laid out in 2-D, the classes are not used. So we can judge how good the algorithm is by seeing if the classes are separated.
  • 7. Displaying the structure of a set of documents using a deep neural network
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. Some fits to the data: which is best? from Bishop
  • 21.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31. Bayes Theorem Prior probability of weight vector W Posterior probability of weight vector W given training data D Probability of observed data given W joint probability conditional probability
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.