In this one-week course, the foundations of machine learning will be laid out and commonly used methods for unsupervised (clustering, dimensionality reduction, visualization) and supervised (mainly classification) learning will be explained in detail. Methods will be illustrated using recent examples from the fields of systems biology and bioinformatics. Methods discussed in the lectures will be put into practice during the computer lab sessions.
Co-organisers
- Amsterdam UMC
- Delft University of Technology
- Netherlands Cancer Institute
Course coordinator(s)
- Perry Moerland, Amsterdam UMC (location AMC)
Course credits
1.5 ECTS for following the course, an additional 1.5 ECTS when successfully completing a final assignment.
Course description
Modern biology is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels. Interpretation of these large-scale biological data requires the detection of statistical dependencies and patterns in order to establish useful models of complex biological systems. Techniques from machine learning are key in this endeavour. Examples are the visualization of single-cell RNA-seq data using dimensionality reduction methods, base calling for nanopore sequencing data using (recurrent) neural networks, and classification of high-throughput microscopy image data using convolutional neural networks. In this one-week course, the foundations of machine learning will be laid out and commonly used methods for unsupervised (clustering, dimensionality reduction, visualization) and supervised (mainly classification) learning will be explained in detail. Methods will be illustrated using recent examples from the fields of systems biology and bioinformatics. Methods discussed in the lectures will be put into practice during the computer lab sessions.
Topics include:
- Density estimation, including nearest neighbour, Parzen
- Performance evaluation, including ROC, cross-validation
- Parametric and non-parametric classifiers, including linear discriminant analysis, k-nearest neighbours, logistic regression, decision trees and random forests
- Feature selection, including search algorithms (forward, backward, branch & bound) and sparse classifiers (ridge, lasso)
- Dimensionality reduction, including principal component analysis, multi-dimensional scaling, t-SNE.
- Clustering, including hierarchical clustering, k-means, Gaussian mixture models
- (Deep) Neural networks
- Kernel-based methods, including support vector machines
- Recent developments: variational autoencoders, diffusion models
Learning objectives
After having followed this course, the student has a good understanding of a wide range of machine learning techniques and is able to recognize what method is most applicable to data analysis problems (s)he encounters in bioinformatics and systems biology applications.
Target audience
The course is aimed at PhD students with a background in bioinformatics, systems biology, 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.
Lecturers
- Marcel Reinders (Delft University of Technology)
- Lodewyk Wessels (Netherlands Cancer Institute)
- Perry Moerland (Amsterdam UMC, location AMC)
Additional information
The computer labs make use of Matlab. The free trial version of Matlab and some toolboxes should be installed on your laptop before the start of the course (and detailed instructions for this will be provided). Note that prior experience with Matlab is not needed.
For more information about the course you can contact Perry Moerland.
Wildcards
If you would like to join this course using a wildcard, please contact the community manager, Petra Aarnoutse.
Until the maximum number of wildcard spots is reached, you can register with a wildcard until December 9, 2024.
Always mention in your application from which academic group the wildcard comes. Your registration is only valid after confirmation by the community manager.
Registration
- € 480 (excl. VAT) for PhD/MSc students
- € 720 (excl. VAT) for academic researchers (non-profit)
- € 1080 (excl. VAT) industry participants (for profit)
Course fees includes course materials and catering (coffee, tea and lunch)
You can register for the course by filling out this registration form.
There is room for max. 25 participants.
Find general enrollment information here.
