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Pattern Recognition

A biennial course, part of the BioSB Research School

  • dr. ir. Perry Moerland (Academic Medical Center)
  • prof. dr. ir. Marcel Reinders (Delft University of Technology)
  • prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

Course coordinator:

Learning objectives

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

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.


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.

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.


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:

  • 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.

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

The course material is available here and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes.

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.
  • 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.


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 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.


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
  • 13.00 - 17.00 Hands-on computer lab (L01-211 (Mo,Th, Fr), L0-211 (Tu, We))

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
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 (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.

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.

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.

Topic revision: r35 - 29 Sep 2017, PerryMoerland Search
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