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I Nyhetsflödet hittar du uppdateringar på sidor, schema och inlägg från lärare (när de även behöver nå tidigare registrerade studenter).
Me too.
I was finally registered (probably by my coordinator).
Lärare Atsuto Maki redigerade 27 augusti 2014
Fältövöreläsning
Lärare Atsuto Maki redigerade 29 augusti 2014
Teacher: Atsuto Maki¶
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 6, Classification Introduction
Schemahandläggare redigerade 1 september 2014
FR4
Fältövöreläsning
Schemahandläggare redigerade 5 september 2014
[{u'user_idname': u'u12rf6rnAtsuto Maki', u'user_nameid': u'Giampiero Salviu1elx760'}]
Lärare Atsuto Maki redigerade 15 september 2014
Lecture 6, ClassificatRegression Introduction
Teacher: Atsuto Maki¶
Lärare Atsuto Maki redigerade 17 september 2014
Topics:¶
* Function approximation
* Linear regression
* RANSAC
* Nearest Neighbours regression
* Linear regression + regularization
* Ridge regression
* Lasso
Slides from this lecture: Slides for Lecture 6¶
Lärare Atsuto Maki redigerade 4 oktober 2014
Topics:
* Function approximation
* Linear regression
* RANSAC
* Nearest Neighbours regression
* Linear regression + regularization
* Ridge regression
* Lasso
Slides from this lecture: Slides for Lecture 6
Lärare Atsuto Maki redigerade 5 oktober 2014
Topics:
* Function approximation
* Linear regression
* RANSAC
* Nearest Neighbours regression
* Linear regression + regularization
* Ridge regression
* Lasso
Slides from this lecture: Slides for Lecture 6or Lecture 6¶
Related reading:¶
Chapter 3 and 6.2 from An Introduction to Statistical Learning (Springer, 2013)¶
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/¶
Lärare Atsuto Maki redigerade 15 oktober 2014
Topics:
* Function approximation
* Linear regression
* RANSAC
* Nearest Neighbours regression
* Linear regression + regularization
* Ridge regression
* Lasso
Slides for Lecture 6
Related reading:
Chapter 3 and 6.2 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 9 juni 2015
Topics:
* Function approximation
* Linear regression
* RANSAC
* Nearest Neighbours regression
* Linear regression + regularization
* Ridge regression
* Lasso
Slides for Lecture 6
Related reading:
Chapter 3 and 6.2 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 10, Ensemble Methods
Lärare Atsuto Maki redigerade 1 oktober 2014
Topics:¶
* Wisdom of Crowd
* Why combine classifiers?
* Bagging
* Decision Forests
* Boosting
Slides for Lecture 10¶
Lärare Atsuto Maki redigerade 2 oktober 2014
Topics:
* Wisdom of Crowd
* Why combine classifiers?
* Bagging
* Decision Forests
* Boosting
Slides for Lecture 10
¶
Lärare Atsuto Maki redigerade 4 oktober 2014
Topics:
* Wisdom of Crowd
* Why combine classifiers?
* Bagging
* Decision Forests
* Boosting
Slides for Lecture 10
Lärare Atsuto Maki redigerade 5 oktober 2014
Topics:
* Wisdom of Crowd
* Why combine classifiers?
* Bagging
* Decision Forests
* Boosting
Slides for Lecture 10
Related reading:¶
Chapter 8.2 from An Introduction to Statistical Learning (Springer, 2013)¶
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 9 juni 2015
Topics:
* Wisdom of Crowd
* Why combine classifiers?
* Bagging
* Decision Forests
* Boosting
Slides for Lecture 10
Related reading:
Chapter 8.2 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 9, Learning Theory
Lärare Örjan Ekeberg redigerade 29 september 2014
Topics:¶
* Concepts and Hypotheses
* PAC-Learning
* VC-Dimension
Slides from lecture 9¶
Lärare Atsuto Maki redigerade 4 oktober 2014
Topics:
* Concepts and Hypotheses
* PAC-Learning
* VC-Dimension
Slides from lecture 9
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 8, Classification with Separating Hyperplanes
Lärare Örjan Ekeberg redigerade 24 september 2014
Topics:¶
* Linear separation
* Structural risk minimization
* Support vector machines
* Kernels
* Non-separable Classes
Slides from lecture 8¶
Lärare Atsuto Maki redigerade 4 oktober 2014
Topics:
* Linear separation
* Structural risk minimization
* Support vector machines
* Kernels
* Non-separable Classes
Slides from lecture 8
Lärare Atsuto Maki redigerade 29 augusti 2014
Teacher: Atsuto Maki¶
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 7, Regression Introduction
Schemahandläggare redigerade 5 september 2014
[{u'user_idname': u'u12rf6rnAtsuto Maki', u'user_nameid': u'Giampiero Salviu1elx760'}]
Lärare Atsuto Maki redigerade 15 september 2014
Lecture 7, RegressClassification Introduction
Teacher: Atsuto Maki¶
Lärare Atsuto Maki redigerade 21 september 2014
Topics:¶
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model
* Naive Bayes
Slides from this lecture: Slides for Lecture 7 ¶
Slides for Lecture 7 (part II)¶
Lärare Atsuto Maki redigerade 22 september 2014
Topics:
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model
* Naive Bayes
Slides from this lecture: Slides for Lecture 7 ¶ (part I) , Slides for Lecture 7 (part II)
¶
Lärare Atsuto Maki redigerade 22 september 2014
Topics:
* Naive Bayes
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model * Naive Bayes Slides from this lecture: Slides for Lecture 7 (part I) , Slides for Lecture 7 (part II)
Lärare Atsuto Maki redigerade 4 oktober 2014
Topics:
* Naive Bayes
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model
Slides from this lecture: Slides for Lecture 7 (part I) , Slides for Lecture 7 (part II)
Lärare Atsuto Maki redigerade 5 oktober 2014
Topics:
* Naive Bayes
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model
Slides from this lecture: or Lecture 7 (part I) , Slides for Lecture 7 (part I) , Slides for Lecture 7 (part II)¶ I)¶
Related reading:¶
Chapter 4 from An Introduction to Statistical Learning (Springer, 2013)¶
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 9 juni 2015
Topics:
* Naive Bayes
* Logistic regression
* Inference and decision
* Discriminative function
* Discriminative vs Generative model
Slides for Lecture 7 (part I) , Slides for Lecture 7 (part II)
Related reading:
Chapter 4 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsLecture 5, Challenges to machine learning
Lärare Atsuto Maki redigerade 14 september 2014
Topics:¶
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5¶
Related reading: Chapter 2 from An Introduction to Statistical Learning James et al.¶
available online at: http://www-bcf.usc.edu/~gareth/ISL/data.html¶
Lärare Atsuto Maki redigerade 14 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
Related reading: Chapter 2 from An Introduction to Statistical Learning James et al.¶ available online at: http://www-bcf.usc.edu/~gareth/ISL/data.html
Lärare Atsuto Maki redigerade 14 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
Lärare Atsuto Maki redigerade 15 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
Lärare Atsuto Maki redigerade 15 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
¶
Lärare Atsuto Maki redigerade 15 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
¶
Lärare Atsuto Maki redigerade 15 september 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5
Lärare Atsuto Maki redigerade 5 oktober 2014
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides from this lecture: Slides for Lecture 5¶ or Lecture 5¶
Related reading:¶
Chapter 2 and 6.4 from An Introduction to Statistical Learning (Springer, 2013)¶
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Atsuto Maki redigerade 9 juni 2015
Topics:
* Challenges to machine learning
* Model complexity and overfitting
* The curse of dimentsionality
* Concepts of prediction errors
* The bias-variance trade-off
Slides for Lecture 5
Related reading:
Chapter 2 and 6.4 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/
Lärare Giampiero Salvi redigerade 8 juli 2014
Teacher: Giampiero Salvi¶
Lärare Atsuto Maki redigerade 29 augusti 2014
FöreläsningLecture 4, Probability II
Lärare Giampiero Salvi redigerade 3 september 2014
Teacher: Giampiero Salvi
Topics:¶
* Fitting probability models (continued)
* Model selection (Occam's Razor)
* Unsupervised learning and Expectation Maximization
Selected reading:¶
Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Princeavailable online at: http://www.computervisionmodels.com/¶
Schemahandläggare redigerade 5 september 2014
[{u'user_idname': u'u1oppompGiampiero Salvi', u'user_nameid': u'\xd6rjan Ekebergu12rf6rn'}]
Lärare Giampiero Salvi redigerade 10 september 2014
Teacher: Giampiero Salvi
Topics:
* Fitting probability models (continued)
* Model selection (Occam's Razor)
* Unsupervised learning and Expectation Maximization
Selected reading:
Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Princeavailable online at: http://www.computervisionmodels.com/
Handouts: Handouts for Lecture 04¶
¶
Lärare Giampiero Salvi redigerade 10 september 2014
Teacher: Giampiero Salvi
Topics:
* Fitting probability models (continued)
* Model selection (Occam's Razor)
* Unsupervised learning and Expectation Maximization
Selected reading:
Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Princeavailable online at: http://www.computervisionmodels.com/
Handouts: Handouts for Lecture 04
Lärare Giampiero Salvi redigerade 11 september 2014
Teacher: Giampiero Salvi
Topics:
* Fitting probability models (continued)
* Model selection (Occam's Razor)
* Unsupervised learning and Expectation Maximization
Selected reading:
Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Princeavailable online at: http://www.computervisionmodels.com/
Handouts: Handouts for Lecture 04
Lärare Atsuto Maki redigerade 4 oktober 2014
Teacher: Giampiero Salvi
Topics:
* Fitting probability models (continued)
* Model selection (Occam's Razor)
* Unsupervised learning and Expectation Maximization
Selected reading:
Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Princeavailable online at: http://www.computervisionmodels.com/
Handouts: Handouts for Lecture 04
This says "The address given is protected by one of the group's administrators." ; I'm "admitted" to this course but still not registered. Is there something I'm supposed to do or the administration will eventually register me?