Difference: PatternRecognitionMaterials (1 vs. 19)

Revision 19
12 Sep 2019 - Main.PerryMoerland
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META TOPICPARENT name="PatternRecognition"
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The first BioSB Machine Learning for Bioinformatics & Systems Biology Course will be given October 7-11, 2019, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed.
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The fifth BioSB Pattern Recognition Course will be given September 25-29, 2017, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed.
 

Note that some of the course material is still likely to change before the course week.
Revision 18
11 Sep 2019 - Main.PerryMoerland
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META TOPICPARENT name="PatternRecognition"
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Revision 17
11 Sep 2019 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Changed:
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Pattern recognition
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Machine Learning for Bioinformatics & Systems Biology
 
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A biennial course, part of the BioSB Research School
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A yearly course, part of the BioSB Research School
 

Lecturers
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  • dr. ir. Perry Moerland (Academic Medical Center)
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  • dr. ir. Perry Moerland (Amsterdam UMC, location: Academic Medical Center)
 
  • prof. dr. ir. Marcel Reinders (Delft University of Technology)
  • prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

Course coordinator:
  • dr. ir. Perry Moerland
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  • telephone: +31 20 5666945
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The fifth BioSB Pattern Recognition Course will be given September 25-29, 2016, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed.
>
>
The first BioSB Machine Learning for Bioinformatics & Systems Biology Course will be given October 7-11, 2019, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed.
 

Note that some of the course material is still likely to change before the course week.
Revision 16
29 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Line: 51 to 51
 
    • Shogun, a Matlab toolbox focusing on large scale kernel methods
    • R is becoming ever more popular for solving data analysis problems. Here is a short reference that provides a mapping between Matlab and R commands.
    • R packages relevant for some of the topics treated in the course are (spread out over a whole range of packages, list is far from complete):
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      • First have a look at caret which provides a really nice set of functions that attempt to streamline the process for creating predictive models.
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      • First have a look at mlr which is the machine learning package in R.
      • Then have a look at caret which also provides a nice set of functions that attempt to streamline the process for creating predictive models.
 
      • e0171: support vector machines and a flexible framework for cross-validation/bootstrapping using the tune function
      • MCResimate: flexible framework for feature selection and cross-validation providing a wrapper for several classifiers (PAM, SVM, random forests, ...). Easily extended with classifiers available in other packages
      • MASS: dla, qda
Revision 15
25 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Line: 40 to 40
 

Changed:
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To use the code and data, download both ZIP files, unpack in the same directory and run prstartup from the Matlab command prompt. A not too old version of Matlab (R2006a or newer) is required.
>
>
To use the code and data, download the ZIP file, unpack everything in the same directory and run prstartup from the Matlab command prompt. A not too old version of Matlab (R2006a or newer) is required.
 

  • Additional tools (not required for the course, but perhaps interesting):
    • GenLab and PRLab (ZIP), a GUI for microarray data analysis, clustering and classification (poorly maintained, use at your own risk!)
Revision 14
25 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu
Line: 24 to 24
 
Changed:
<
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The fifth BioSB Pattern Recognition Course will be given September 25-29, 2016, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
>
>
The fifth BioSB Pattern Recognition Course will be given September 25-29, 2016, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed.
 

Note that some of the course material is still likely to change before the course week.
Revision 13
15 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu

Line: 24 to 24
 
Changed:
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The fifth BioSB Pattern Recognition Course will be given in Autumn 2017, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
>
>
The fifth BioSB Pattern Recognition Course will be given September 25-29, 2016, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
 
Changed:
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Note that for the moment all course material is from the 2015 edition of the course.
>
>
Note that some of the course material is still likely to change before the course week.
 

  • To prepare for the course:
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have a lot of trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
Line: 40 to 40
 

Changed:
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    • the Matlab toolboxes used (ZIP), among which PRTools is the most important; for the Matlab version installed at the VU (R2014a) also download some patches
>
>
 
Changed:
<
<
To use the code and data, download both ZIP files, unpack in the same directory and run prstartup from the Matlab command prompt. A recent version of Matlab (R2006a or newer) is required.
>
>
To use the code and data, download both ZIP files, unpack in the same directory and run prstartup from the Matlab command prompt. A not too old version of Matlab (R2006a or newer) is required.
 

  • Additional tools (not required for the course, but perhaps interesting):
    • GenLab and PRLab (ZIP), a GUI for microarray data analysis, clustering and classification (poorly maintained, use at your own risk!)
Revision 12
05 Jul 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu
Line: 24 to 24
 
Added:
>
>
The fifth BioSB Pattern Recognition Course will be given in Autumn 2017, at the Academic Medical Center, Amsterdam. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
 
Changed:
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The fourth BioSB Pattern Recognition Course will be given in Spring 2015, at VU University. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
>
>
Note that for the moment all course material is from the 2015 edition of the course.
 

  • To prepare for the course:
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have a lot of trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
Line: 37 to 38
 

Deleted:
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Note that a hardcopy of the handouts and lab course manual (below) will be supplied to students at the start of the course.
 
  • Material used during the lab course:
Revision 11
16 Oct 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Revision 10
25 Mar 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu
Line: 53 to 53
 
    • Shogun, a Matlab toolbox focusing on large scale kernel methods
    • R is becoming ever more popular for solving data analysis problems. Here is a short reference that provides a mapping between Matlab and R commands.
    • R packages relevant for some of the topics treated in the course are (spread out over a whole range of packages, list is far from complete):
Added:
>
>
      • First have a look at caret which provides a really nice set of functions that attempt to streamline the process for creating predictive models.
 
      • e0171: support vector machines and a flexible framework for cross-validation/bootstrapping using the tune function
      • MCResimate: flexible framework for feature selection and cross-validation providing a wrapper for several classifiers (PAM, SVM, random forests, ...). Easily extended with classifiers available in other packages
      • MASS: dla, qda
Line: 62 to 63
 

Some good material for further reading:
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Line: 70 to 71
 
Changed:
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Revision 9
22 Mar 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu
Line: 41 to 41
 

Changed:
<
<
>
>
    • the Matlab toolboxes used (ZIP), among which PRTools is the most important; for the Matlab version installed at the VU (R2014a) also download some patches
 

To use the code and data, download both ZIP files, unpack in the same directory and run prstartup from the Matlab command prompt. A recent version of Matlab (R2006a or newer) is required.
Revision 8
10 Mar 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Back to menu
Line: 33 to 33
 

The lab courses will make extensive use of Matlab. You do not need to be a fluent programmer, but if you have never worked with Matlab before it may help to try to get a hold of a copy of Matlab (your university may have a campus license) and have a look at the Appendices of the lab course manual (see below). An extensive Matlab primer is also available. During the course Matlab and all software/data are available on the PCs in the lab, so there is no need to bring your laptop.
Changed:
<
<
  • Material used during the lectures (2013 version, will be updated for the 2015 course):
>
>
  • Material used during the lectures:
 

Note that a hardcopy of the handouts and lab course manual (below) will be supplied to students at the start of the course.
Changed:
<
<
  • Material used during the lab course (2013 version, will be updated for the 2015 course):
>
>
  • Material used during the lab course:
 
Revision 7
22 Jan 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Line: 70 to 69
 
Changed:
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A.K. Jain, R.P.W. Duin and J. Mao, Statistical pattern recognition: a review, IEEE Tr. on Pattern Analysis and Machine Intelligence 22(1):4-37, 2000.
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Revision 6
11 Jan 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Line: 29 to 29
  The fourth BioSB Pattern Recognition Course will be given in Spring 2015, at VU University. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:

  • To prepare for the course:
Changed:
<
<
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
>
>
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have a lot of trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
 
    • a few primers (ZIP/PDF, 4.9 Mb) on these topics.

The lab courses will make extensive use of Matlab. You do not need to be a fluent programmer, but if you have never worked with Matlab before it may help to try to get a hold of a copy of Matlab (your university may have a campus license) and have a look at the Appendices of the lab course manual (see below). An extensive Matlab primer is also available. During the course Matlab and all software/data are available on the PCs in the lab, so there is no need to bring your laptop.
Changed:
<
<
  • Material used during the lectures:
>
>
  • Material used during the lectures (2013 version, will be updated for the 2015 course):
 

Note that a hardcopy of the handouts and lab course manual (below) will be supplied to students at the start of the course.
Changed:
<
<
  • Material used during the lab course:
>
>
  • Material used during the lab course (2013 version, will be updated for the 2015 course):
 
Revision 5
29 Sep 2014 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
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Medical Bioinformatics

 
Back to menu

Line: 14 to 13
 
Pattern recognition
Changed:
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A biennial course, part of the NBIC PhD School
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A biennial course, part of the BioSB Research School
 

Lecturers
Deleted:
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<
  • dr. ir. Dick de Ridder (Delft University of Technology)
 
  • dr. ir. Perry Moerland (Academic Medical Center)
Added:
>
>
  • prof. dr. ir. Marcel Reinders (Delft University of Technology)
 
  • prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

Course coordinator:
Line: 27 to 26
 
  • telephone: +31 20 5666945
Changed:
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The third NBIC Pattern Recognition Course will be given January 21-25 2011, at VU University. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
>
>
The fourth BioSB Pattern Recognition Course will be given in Spring 2015, at VU University. General information on the course can be found here. This page only contains the material used during the course; you can download it to work at your own institution. Note that this material is free for academic use only and should not be redistributed:
 

  • To prepare for the course:
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
Revision 4
25 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Medical Bioinformatics

Line: 52 to 52
 
    • GenLab and PRLab (ZIP), a GUI for microarray data analysis, clustering and classification (poorly maintained, use at your own risk!)
    • BRB ArrayTools, an Excel-based microarray data analysis package using R in the background
    • WEKA, a Java-based collection of machine learning algorithms for data mining
Added:
>
>
    • Shogun, a Matlab toolbox focusing on large scale kernel methods
 
    • R is becoming ever more popular for solving data analysis problems. Here is a short reference that provides a mapping between Matlab and R commands.
Added:
>
>
    • R packages relevant for some of the topics treated in the course are (spread out over a whole range of packages, list is far from complete):
 

Some good material for further reading:

Added:
>
>
 
Revision 3
23 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Medical Bioinformatics

Line: 52 to 52
 
    • GenLab and PRLab (ZIP), a GUI for microarray data analysis, clustering and classification (poorly maintained, use at your own risk!)
    • BRB ArrayTools, an Excel-based microarray data analysis package using R in the background
    • WEKA, a Java-based collection of machine learning algorithms for data mining
Added:
>
>
    • R is becoming ever more popular for solving data analysis problems. Here is a short reference that provides a mapping between Matlab and R commands.
 

Some good material for further reading:
Revision 2
15 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="PatternRecognition"
Medical Bioinformatics

Line: 33 to 33
 
    • a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have trouble answering some of these exercises, consult the text books mentioned in the PDF, or:
    • a few primers (ZIP/PDF, 4.9 Mb) on these topics.
Changed:
<
<
The lab courses will make extensive use of Matlab. You do not need to be a fluent programmer, but if you have never worked with Matlab before it may help to try to get a hold of a copy of Matlab (your university may have a campus license) and have a look at the Appendices of the lab course manual (see below). An extensive Matlab primer is also available.
>
>
The lab courses will make extensive use of Matlab. You do not need to be a fluent programmer, but if you have never worked with Matlab before it may help to try to get a hold of a copy of Matlab (your university may have a campus license) and have a look at the Appendices of the lab course manual (see below). An extensive Matlab primer is also available. During the course Matlab and all software/data are available on the PCs in the lab, so there is no need to bring your laptop.
 

  • Material used during the lectures:
    • handouts (ZIP/PDF) of the slides used during the course.
 
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