Difference: PatternRecognition (1 vs. 35)

Revision 35
29 Sep 2017 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 86 to 86
  Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, Expectation-Maximization. Hidden Markov models.
Changed:
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Friday (September 29) - Feature selection and extraction
>
>
Friday (September 29) - Feature selection and extraction
  Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
Revision 34
28 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
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Line: 82 to 82
  Lecturer Marcel Reinders
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation. Deep learning.
Changed:
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<
Thursday (September 28) - Clustering and HMMs
>
>
Thursday (September 28) - Clustering and HMMs
  Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, Expectation-Maximization. Hidden Markov models.
Revision 33
27 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
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Line: 78 to 78
  Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Changed:
<
<
Wednesday (September 27) - Feature selection and extraction
Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
>
>
Wednesday (September 27) - Selected advanced topics
Lecturer Marcel Reinders
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation. Deep learning.
 

Thursday (September 28) - Clustering and HMMs
Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, Expectation-Maximization. Hidden Markov models.
Changed:
<
<
Friday (September 29) - Selected advanced topics
Lecturer Marcel Reinders
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation. Deep learning.
>
>
Friday (September 29) - Feature selection and extraction
Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
 

For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
Revision 32
26 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
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Line: 74 to 74
  Lecturer Marcel Reinders
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Changed:
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<
Tuesday (September 26) - Classifiers
>
>
Tuesday (September 26) - Classifiers
  Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Revision 31
25 Sep 2017 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 68 to 68
 
  • 12.00 - 13.00 Lunch
  • 13.00 - 17.00 Hands-on computer lab (L01-211 (Mo,Th, Fr), L0-211 (Tu, We))
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L01-211 and L0-211 are located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
>
>
L01-211 (basement) and L0-211 (ground floor) are located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
 

Monday (September 25) - Introduction
Lecturer Marcel Reinders
Revision 30
25 Sep 2017 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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L01-211 and L0-211 are located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
Changed:
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<
Monday (September 25) - Introduction
>
>
Monday (September 25) - Introduction
  Lecturer Marcel Reinders
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Revision 29
15 Sep 2017 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 62 to 62
 

Schedule

Changed:
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The course will run September 25-29 2017. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day will have the following layout:
>
>
The course will run September 25-29 2017. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day (except Thursday when we will have to start at 8:30) will have the following layout:
 

  • 9.00 - 12.00 Lectures (L01-211 (Mo,Th, Fr), L0-211 (Tu, We))
  • 12.00 - 13.00 Lunch
Revision 28
15 Sep 2017 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
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Pattern Recognition

Line: 50 to 50
 

Course material

Changed:
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<
The course material (from the 2015 edition) is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes.
>
>
The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes.
 
Changed:
<
<
For the moment you are already advised to have a look at the following documents (from the 2015 edition of the course):
>
>
For the moment you are already advised to have a look at the following documents:
 
  • 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 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. 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.
>
>
  • 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) before the course and have a look at the Appendices of the lab course manual. 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.
 

Examination

Line: 64 to 64
 

The course will run September 25-29 2017. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day will have the following layout:
Changed:
<
<
  • 9.30 - 12.30 Lectures (L01-211)
  • 12.30 - 13.30 Lunch
  • 13.30 - 17.30 Hands-on computer lab (L01-211)
>
>
  • 9.00 - 12.00 Lectures (L01-211 (Mo,Th, Fr), L0-211 (Tu, We))
  • 12.00 - 13.00 Lunch
  • 13.00 - 17.00 Hands-on computer lab (L01-211 (Mo,Th, Fr), L0-211 (Tu, We))
 
Changed:
<
<
L01-211 is located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
>
>
L01-211 and L0-211 are located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
 

Monday (September 25) - Introduction
Lecturer Marcel Reinders
Revision 27
16 Jul 2017 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 29 to 29
 

Target audience

Changed:
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The course is aimed at PhD students with a background in bioinformatics, computer science or a related field, or life sciences. Participants from the private sector are also welcome. A working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
>
>
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field, and life sciences. Participants from the private sector are also welcome. A working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
 

Description

Line: 46 to 46
 
  • Course material: Lecture slides, a lab course manual and software required for the lab course (MATLAB toolboxes) will be made available online.
  • Catering: Coffee, tea, soft drinks and lunch will be provided.
Changed:
<
<
Information about hostel accommodation in Amsterdam can be found [https://www.vu.nl/en/programmes/links/hotels.aspx][here]]. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
>
>
Information about hostel accommodation in Amsterdam can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
 

Course material

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05 Jul 2017 - Main.PerryMoerland
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Objectives

>
>

Learning objectives

 
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After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.
>
>
After having followed this course, the student has a good understanding of basic pattern recognition and machine learning techniques and is able to recognize what method is most applicable to data analysis problems (s)he encounters in bioinformatics applications.
 

Target audience

Changed:
<
<
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
>
>
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field, or life sciences. Participants from the private sector are also welcome. A working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
 

Description

Changed:
<
<
Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, ?) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly formulate such an algorithm. In such cases, a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (microarray data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.
>
>
Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, …) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly design such an algorithm. In such cases, often a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (gene expression data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.
 
Changed:
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In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The first two days will introduce the basic classification problem and a number of classic approaches to solve it. Next, methods for selecting or extracting informative features from a large set of measurements will be introduced. This will be followed by an introduction to a number of unsupervised techniques, that allow to find natural groupings or probabilistic descriptions of (unlabeled) data. The course will end with a cursory introduction to a number of intricate classifiers, artificial neural networks and support vector machines, and an overview of approaches to solve the generalization problem. For a large number of the methods discussed, we will turn to recent bioinformatics literature for examples.
>
>
In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The course is a mixture of theory sessions and lab courses. During the lab courses Matlab will be used and a brief introduction to Matlab will be provided. The course has to be completed afterwards with a 5-10 page report describing the analysis of a biological dataset using some of the methods taught in the course.
 

Registration

Changed:
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You can pre-register for this course by filling out the BioSB enrolment form. The maximum number of participants is 25, so register soon to be sure of a course seat!
>
>
You can register for this course by filling out the BioSB enrolment form. The maximum number of participants is 25, so register soon to be sure of a course seat!
 

The course fee (to be determined) includes:
Changed:
<
<
  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Monday, March 23 at 5:30pm.
>
>
  • Course material: Lecture slides, a lab course manual and software required for the lab course (MATLAB toolboxes) will be made available online.
  • Catering: Coffee, tea, soft drinks and lunch will be provided.
 
Changed:
<
<
Information about hotel accommodation in Amsterdam can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
>
>
Information about hostel accommodation in Amsterdam can be found [https://www.vu.nl/en/programmes/links/hotels.aspx][here]]. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
 

Course material

Changed:
<
<
The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
>
>
The course material (from the 2015 edition) is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes.
 
Changed:
<
<
For the moment you are already advised to have a look at the following documents:
>
>
For the moment you are already advised to have a look at the following documents (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 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. 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.

Examination

Changed:
<
<
Participants requiring a certificate of successful completion should make a final assignment. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished (April 17, 2015). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission. Those who choose not to make the final assignment will receive a certificate of participation.
>
>
Participants requiring a certificate of successful completion should make a final assignment. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished (October 20, 2017). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission. Those who choose not to make the final assignment will receive a certificate of participation.
 

Schedule

Changed:
<
<
The course will run March 23-27, 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day will have the following layout:
>
>
The course will run September 25-29 2017. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day will have the following layout:
 
Changed:
<
<
  • 9.30 - 12.30 Lectures (F630, W&N building)
>
>
  • 9.30 - 12.30 Lectures (L01-211)
 
  • 12.30 - 13.30 Lunch
Changed:
<
<
  • 13.30 - 17.30 Hands-on computer lab (S345, W&N building)
>
>
  • 13.30 - 17.30 Hands-on computer lab (L01-211)
 
Changed:
<
<
"W&N" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
>
>
L01-211 is located in the main building of the Academic Medical Center (map), Meibergdreef 9, Amsterdam. Travel directions can be found here.
 
Changed:
<
<
Monday (March 23) - Introduction
>
>
Monday (September 25) - Introduction
  Lecturer Marcel Reinders
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Changed:
<
<
Tuesday (March 24) - Classifiers
>
>
Tuesday (September 26) - Classifiers
  Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Changed:
<
<
Wednesday (March 25) - Feature selection and extraction
>
>
Wednesday (September 27) - Feature selection and extraction
  Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
Changed:
<
<
Thursday (March 26) - Clustering and HMMs
>
>
Thursday (September 28) - Clustering and HMMs
  Lecturer Perry Moerland
Changed:
<
<
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, EM. Hidden Markov models.
>
>
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, Expectation-Maximization. Hidden Markov models.
 
Changed:
<
<
Friday (March 27) - Selected advanced topics
>
>
Friday (September 29) - Selected advanced topics
  Lecturer Marcel Reinders
Changed:
<
<
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.
>
>
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation. Deep learning.
 
Changed:
<
<
For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
>
>
For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
 


Revision 25
16 Oct 2015 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Pattern Recognition

Revision 24
18 Mar 2015 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 62 to 62
 

Schedule

Changed:
<
<
The course will run March 23-27, 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
>
>
The course will run March 23-27, 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. After the course, 2-3 days will have to be spent on the report to be handed in. Each course day will have the following layout:
 

  • 9.30 - 12.30 Lectures (F630, W&N building)
  • 12.30 - 13.30 Lunch
Revision 23
18 Mar 2015 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Line: 44 to 44
  The course fee (to be determined) includes:

  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
Changed:
<
<
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket.
>
>
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Monday, March 23 at 5:30pm.
 

Information about hotel accommodation in Amsterdam can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
Line: 58 to 58
 

Examination

Changed:
<
<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished. We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Participants requiring a certificate of successful completion should make a final assignment. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished (April 17, 2015). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission. Those who choose not to make the final assignment will receive a certificate of participation.
 

Schedule

Revision 22
10 Mar 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
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Line: 50 to 50
 

Course material

Changed:
<
<
The course material is available here (2013 version, will be updated for the 2015 course) and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
>
>
The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
 

For the moment you are already advised to have a look at the following documents:
  • 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 a few primers (ZIP/PDF, 4.9 Mb) on these topics.
Revision 21
22 Jan 2015 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Back to menu
Line: 62 to 62
 

Schedule

Changed:
<
<
The course will be given in Spring 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
>
>
The course will run March 23-27, 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
 

  • 9.30 - 12.30 Lectures (F630, W&N building)
  • 12.30 - 13.30 Lunch
Line: 70 to 70
 

"W&N" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
Changed:
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Monday - Introduction
>
>
Monday (March 23) - Introduction
  Lecturer Marcel Reinders
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Changed:
<
<
Tuesday - Classifiers
>
>
Tuesday (March 24) - Classifiers
  Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Changed:
<
<
Wednesday - Feature selection and extraction
>
>
Wednesday (March 25) - Feature selection and extraction
  Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
Changed:
<
<
Thursday - Clustering and HMMs
>
>
Thursday (March 26) - Clustering and HMMs
  Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, EM. Hidden Markov models.
Changed:
<
<
Friday - Selected advanced topics
>
>
Friday (March 27) - Selected advanced topics
  Lecturer Marcel Reinders
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.
Revision 20
11 Jan 2015 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
Back to menu
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Course material

Changed:
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The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
>
>
The course material is available here (2013 version, will be updated for the 2015 course) and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
 

For the moment you are already advised to have a look at the following documents:
Changed:
<
<
  • 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 a few primers (ZIP/PDF, 4.9 Mb) on these topics.
>
>
  • 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 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. 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.

Examination

Line: 64 to 64
 

The course will be given in Spring 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
Changed:
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<
  • 9.30 - 12.30 Lectures
>
>
  • 9.30 - 12.30 Lectures (F630, W&N building)
 
  • 12.30 - 13.30 Lunch
Changed:
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<
  • 13.30 - 17.30 Hands-on computer lab
>
>
  • 13.30 - 17.30 Hands-on computer lab (S345, W&N building)
 
Changed:
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<
"WN" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
>
>
"W&N" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
 

Monday - Introduction
Lecturer Marcel Reinders
Revision 19
29 Sep 2014 - Main.PerryMoerland
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Pattern recognition
>
>

Pattern Recognition

  A biennial course, part of the BioSB Research School

Lecturers
Revision 18
29 Sep 2014 - Main.PerryMoerland
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META TOPICPARENT name="EducationBioLab"
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Medical Bioinformatics

 
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Pattern recognition
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A biennial course, part of the NBIC PhD School
>
>
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:
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>
  • prof. dr. ir. Marcel Reinders (Delft University of Technology)
 
  • prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

Course coordinator:
Line: 43 to 42
 

Registration

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You can register for this course by filling out the NBIC enrolment form. The he maximum number of participants is 25, so register soon to be sure of a course seat! Should the course be overbooked, PhD-students in the BioRange programme will be allowed access first.
>
>
You can pre-register for this course by filling out the BioSB enrolment form. The maximum number of participants is 25, so register soon to be sure of a course seat!
 
Changed:
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<
The course is free for NBIC and SIB affiliated PhD students; more information about the course fees can be found at the enrolment page. The fee includes:
>
>
The course fee (to be determined) includes:
 

  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
Changed:
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<
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Monday January 21 at 17h30.
>
>
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket.
 
Changed:
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<
Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
>
>
Information about hotel accommodation in Amsterdam can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
 

Course material

Line: 62 to 61
 

Examination

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<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished. We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
 

Schedule

Changed:
<
<
The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
>
>
The course will be given in Spring 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
 
Changed:
<
<
  • 9.30 - 12.30 Lectures (room WN-S655, on Friday: room WN-F607)
  • 12.30 - 13.30 Lunch (canteen WN-G070)
  • 13.30 - 17.30 Hands-on computer lab (room WN-P323)
>
>
  • 9.30 - 12.30 Lectures
  • 12.30 - 13.30 Lunch
  • 13.30 - 17.30 Hands-on computer lab
 

"WN" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
Changed:
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Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
>
>
Monday - Introduction
Lecturer Marcel Reinders
  Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Deleted:
<
<
17h30: Drinks and Bio-Café contest in The Basket (on the VU campus)
 
Changed:
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Tuesday (22-1-2013) Classifiers
>
>
Tuesday - Classifiers
  Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Changed:
<
<
Wednesday (23-1-2013) Feature selection and extraction
>
>
Wednesday - Feature selection and extraction
  Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
Changed:
<
<
Thursday (24-1-2013) Clustering and HMMs
>
>
Thursday - Clustering and HMMs
  Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, EM. Hidden Markov models.
Changed:
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<
Friday (25-1-2013) Selected advanced topics
Lecturer Dick de Ridder
>
>
Friday - Selected advanced topics
Lecturer Marcel Reinders
  Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.
Changed:
<
<
For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
>
>
For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
 


Revision 17
01 Aug 2014 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 77 to 77
  Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Changed:
<
<
17h30: Drinks and Bio-Caf contest in The Basket (on the VU campus)
>
>
17h30: Drinks and Bio-Café contest in The Basket (on the VU campus)
 

Tuesday (22-1-2013) Classifiers
Lecturer Perry Moerland
Revision 16
25 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 62 to 62
 

Examination

Changed:
<
<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to p.d.moerland@amc.uva.nl no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
 

Schedule

Revision 15
25 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 68 to 68
 

The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
Changed:
<
<
  • 9.30 - 12.30 Lectures (room WN-S655)
>
>
  • 9.30 - 12.30 Lectures (room WN-S655, on Friday: room WN-F607)
 
  • 12.30 - 13.30 Lunch (canteen WN-G070)
  • 13.30 - 17.30 Hands-on computer lab (room WN-P323)
Revision 14
23 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 85 to 85
 

Wednesday (23-1-2013) Feature selection and extraction
Lecturer Lodewyk Wessels
Changed:
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<
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS, LLE, Isomap.
>
>
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.
 

Thursday (24-1-2013) Clustering and HMMs
Lecturer Perry Moerland
Revision 13
16 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 69 to 69
  The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:

  • 9.30 - 12.30 Lectures (room WN-S655)
Changed:
<
<
  • 12.30 - 13.30 Lunch
>
>
  • 12.30 - 13.30 Lunch (canteen WN-G070)
 
  • 13.30 - 17.30 Hands-on computer lab (room WN-P323)

"WN" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
Revision 12
15 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 58 to 58
 

For the moment you are already advised to have a look at the following documents:
  • 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 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. 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. 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.
 

Examination

Revision 11
08 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 77 to 77
  Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Changed:
<
<
17h30: Drinks in The Basket
>
>
17h30: Drinks and Bio-Caf contest in The Basket (on the VU campus)
 

Tuesday (22-1-2013) Classifiers
Lecturer Perry Moerland
Revision 10
08 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 9 to 9
  -->
Changed:
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<

Pattern Recognition

A biennial course, part of the NBIC PhD School
>
>
Pattern recognition
A biennial course, part of the NBIC PhD School
 

Lecturers
  • dr. ir. Dick de Ridder (Delft University of Technology)
Line: 47 to 48
  The course is free for NBIC and SIB affiliated PhD students; more information about the course fees can be found at the enrolment page. The fee includes:

  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
Changed:
<
<
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Tuesday January 22 at 17h30.
>
>
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Monday January 21 at 17h30.
 

Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.

Course material

Changed:
<
<
The course material will be made available before the start of the course including handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
>
>
The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.
 
Changed:
<
<
For the moment you are advised to have a look at the following documents:
>
>
For the moment you are already advised to have a look at the following documents:
 
  • 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 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 (2011 version). 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. An extensive Matlab primer is also available.
 

Examination

Changed:
<
<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
 

Schedule

Line: 76 to 77
  Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.
Added:
>
>
17h30: Drinks in The Basket
 

Tuesday (22-1-2013) Classifiers
Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Deleted:
<
<
17h30: Drinks in The Basket
 

Wednesday (23-1-2013) Feature selection and extraction
Lecturer Lodewyk Wessels
Line: 95 to 96
  Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.

For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder. \ No newline at end of file
Added:
>
>


Revision 9
08 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 49 to 49
 
  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Tuesday January 22 at 17h30.
Changed:
<
<
Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
>
>
Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
 

Course material

Line: 61 to 61
 

Examination

Changed:
<
<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 15, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 17, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
 

Schedule

Revision 8
07 Jan 2013 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="EducationBioLab"
Medical Bioinformatics

Line: 30 to 30
 

After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.
Deleted:
<
<

Time and location

The next course will be given January 21-25 2013, at the Faculty of Sciences, VU University, De Boelelaan 1085, Amsterdam, The Netherlands (W&N Building). Travel directions can be found here. The lectures will take place in room WN-S655, computer labs in room WN-P323.
 

Target audience

The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
Line: 51 to 47
  The course is free for NBIC and SIB affiliated PhD students; more information about the course fees can be found at the enrolment page. The fee includes:

  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
Changed:
<
<
  • Catering: coffee, tea and soft drinks and lunch will be provided. Drinks will be organized in the afternoon of Tuesday January 22.
>
>
  • Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket on Tuesday January 22 at 17h30.
 

Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.
Line: 65 to 61
 

Examination

Changed:
<
<
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 15, 2011). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
>
>
Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 15, 2013). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.
 
Changed:
<
<

Format

>
>

Schedule

 
Changed:
<
<
The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have roughly the following layout:
>
>
The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:
 
Changed:
<
<
  • 9.30 - 12.30 Lectures
>
>
  • 9.30 - 12.30 Lectures (room WN-S655)
 
  • 12.30 - 13.30 Lunch
Changed:
<
<
  • 13.30 - 17.30 Hands-on computer lab course
>
>
  • 13.30 - 17.30 Hands-on computer lab (room WN-P323)
 
Changed:
<
<

Tentative schedule

>
>
"WN" means "Wis- en Natuurkundegebouw" (map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found here.
 

Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
Line: 84 to 80
  Tuesday (22-1-2013) Classifiers
Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.
Added:
>
>
17h30: Drinks in The Basket
 

Wednesday (23-1-2013) Feature selection and extraction
Lecturer Lodewyk Wessels
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19 Oct 2012 - Main.PerryMoerland
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A biennial course, part of the NBIC PhD School

Lecturers
  • dr. ir. Dick de Ridder (Delft University of Technology)
  • dr. ir. Perry Moerland (Academic Medical Center)
  • prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

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Lecturers:
  • Dr. ir. Dick de Ridder
  • Dr. Lodewyk Wessels
    • NKI
  • Dr. ir Perry Moerland
    • Bioinformatics Laboratory, Academic Medical Center
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Objectives

 
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After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.
 
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NBIC PhD School

NBIC PhD School: advanced courses for bioinformaticians
In the field of bioinformatics, there is a continuous flow of new insights, tools and applications. The NBIC PhD School targets the need to stay up to date through an advanced programme developed and taught by experts with hands-on experience. The courses cover a variety of topics and technologies and allow the creation of a personalised education programme that specifically fits your research and interests. The NBIC PhD school courses are accessible for PhD students and post-docs worldwide. to broaden the international scope of the NBIC PhD School, partnerships with other institutes, for example with the Swiss Institute for Bioinformatics (SIB), are developed.
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In short, the NBIC PhD School aims to:
  • Offer a top-level education and training programme in bioinformatics
  • Create opportunities for PhD students to broaden their scientific scope
  • Provide an environment for international networking and exchange
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The next course will be given January 21-25 2013, at the Faculty of Sciences, VU University, De Boelelaan 1085, Amsterdam, The Netherlands (W&N Building). Travel directions can be found here. The lectures will take place in room WN-S655, computer labs in room WN-P323.
 
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Pattern recognition module

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After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.
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Target audience

 
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Target audience
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
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The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.

Description

 
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Description
  Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, ?) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly formulate such an algorithm. In such cases, a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (microarray data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.
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In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The first two days will introduce the basic classification problem and a number of classic approaches to solve it. Next, methods for selecting or extracting informative features from a large set of measurements will be introduced. This will be followed by an introduction to a number of unsupervised techniques, that allow to find natural groupings or probabilistic descriptions of (unlabeled) data. The course will end with a cursory introduction to a number of intricate classifiers, artificial neural networks and support vector machines, and an overview of approaches to solve the generalization problem. For a large number of the methods discussed, we will turn to recent bioinformatics literature for examples.
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Examination
The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished.
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Registration

You can register for this course by filling out the NBIC enrolment form. The he maximum number of participants is 25, so register soon to be sure of a course seat! Should the course be overbooked, PhD-students in the BioRange programme will be allowed access first.

The course is free for NBIC and SIB affiliated PhD students; more information about the course fees can be found at the enrolment page. The fee includes:

  • Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
  • Catering: coffee, tea and soft drinks and lunch will be provided. Drinks will be organized in the afternoon of Tuesday January 22.

Information about hotel accommodation in Amsterdam during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.

Course material

The course material will be made available before the start of the course including handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.

For the moment you are advised to have a look at the following documents:
  • 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 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 (2011 version). An extensive Matlab primer is also available.

Examination

Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 15, 2011). We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.

Format

The course will be given in the week of January 21-25, 2013. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have roughly the following layout:

  • 9.30 - 12.30 Lectures
  • 12.30 - 13.30 Lunch
  • 13.30 - 17.30 Hands-on computer lab course

Tentative schedule

Monday (21-1-2013) Introduction
Lecturer Dick de Ridder
Subjects Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.

Tuesday (22-1-2013) Classifiers
Lecturer Perry Moerland
Subjects Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.

Wednesday (23-1-2013) Feature selection and extraction
Lecturer Lodewyk Wessels
Subjects Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS, LLE, Isomap.

Thursday (24-1-2013) Clustering and HMMs
Lecturer Perry Moerland
Subjects Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, EM. Hidden Markov models.

Friday (25-1-2013) Selected advanced topics
Lecturer Dick de Ridder
Subjects Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.

For more information about the course programme, please contact Perry Moerland; for more information about registration or logistics, please contact Celia van Gelder.
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05 Oct 2012 - Main.PerryMoerland
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28 Apr 2011 - Main.AngelaLuijf
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Medical Bioinformatics

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15 Apr 2011 - Main.PerryMoerland
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Examination
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The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 6, 2009).
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The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished.
 
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07 Jun 2010 - Main.PerryMoerland
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  • Provide an environment for international networking and exchange

Pattern recognition module

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*Goal pattern recognition module *
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Goal pattern recognition module
  After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.
Changed:
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<
Target audience
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed.
>
>
Target audience
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed.
  Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.
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Description
>
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Description
  Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, ?) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly formulate such an algorithm. In such cases, a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (microarray data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.

In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The first two days will introduce the basic classification problem and a number of classic approaches to solve it. Next, methods for selecting or extracting informative features from a large set of measurements will be introduced. This will be followed by an introduction to a number of unsupervised techniques, that allow to find natural groupings or probabilistic descriptions of (unlabeled) data. The course will end with a cursory introduction to a number of intricate classifiers, artificial neural networks and support vector machines, and an overview of approaches to solve the generalization problem. For a large number of the methods discussed, we will turn to recent bioinformatics literature for examples.
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Examination
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Examination
  The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 6, 2009).
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13 Mar 2010 - Main.AntoineVanKampen
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META TOPICPARENT name="EducationBioLab"
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Pattern recognition

Under construction.
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under construction

Pattern Recognition

Part of the NBIC PhD school

Lecturers:
  • Dr. ir. Dick de Ridder

  • Dr. Lodewyk Wessels
    • NKI

  • Dr. ir Perry Moerland
    • Bioinformatics Laboratory, Academic Medical Center

NBIC PhD School

NBIC PhD School: advanced courses for bioinformaticians
In the field of bioinformatics, there is a continuous flow of new insights, tools and applications. The NBIC PhD School targets the need to stay up to date through an advanced programme developed and taught by experts with hands-on experience. The courses cover a variety of topics and technologies and allow the creation of a personalised education programme that specifically fits your research and interests. The NBIC PhD school courses are accessible for PhD students and post-docs worldwide. to broaden the international scope of the NBIC PhD School, partnerships with other institutes, for example with the Swiss Institute for Bioinformatics (SIB), are developed.

In short, the NBIC PhD School aims to:
  • Offer a top-level education and training programme in bioinformatics
  • Create opportunities for PhD students to broaden their scientific scope
  • Provide an environment for international networking and exchange

Pattern recognition module

*Goal pattern recognition module *
After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.

Target audience
The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.

Description
Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, ?) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly formulate such an algorithm. In such cases, a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (microarray data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.

In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The first two days will introduce the basic classification problem and a number of classic approaches to solve it. Next, methods for selecting or extracting informative features from a large set of measurements will be introduced. This will be followed by an introduction to a number of unsupervised techniques, that allow to find natural groupings or probabilistic descriptions of (unlabeled) data. The course will end with a cursory introduction to a number of intricate classifiers, artificial neural networks and support vector machines, and an overview of approaches to solve the generalization problem. For a large number of the methods discussed, we will turn to recent bioinformatics literature for examples.

Examination
The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be handed in no later than three weeks after the course has finished (i.e. by February 6, 2009).
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