Teaching

As a bioinformatician I teach courses in various different fields, mainly computer science, statistics, and biology. I regularly teach the following courses:

Data science

Data science in bioinformatics: Visualisation and analysis

The objectives of the class are to obtain familiarity with techniques for data handling and formatting, statistical testing and data mining in large data sets, and visualization of key patterns in large data. The focus is on practical applications of data mining and machine learning techniques, evaluation of performance (both computational and statistical) and ways of visualizing high dimensional data and data patterns.

Data science in bioinformatics: Software development

The objectives of the class are to obtain familiarity with techniques for developing reusable software extensions for data analysis frameworks. This involves data handling and formatting, interface design, and both correctness and statistical testing. The focus is on practical applications in data mining and machine learning.

Computer science

String algorithms

String algorithms (i.e. algorithms and data structures for analysis and indexing of strings) are an important aspect of many computer science disciplines, such as data-compression, cryptography, speech- and image-recognition, and computational biology. Furthermore, string algorithms is an interesting theoretical field in itself, with many fascinating problems and elegant solutions.

This class gives an introduction to string algorithms. The class presents concrete techniques and string-algorithms and their implementation and analysis: exact and approximate pattern-matching; string distance calculations; repetition and periodicity search; construction and applications of suffix-trees and suffix-arrays.

I teach this course together with Christian N. S. Pedersen.

Machine learning

The field machine learning concerns techniques for programming a computer to learn from data. A traditional program can be seen as implementing a number of concrete rules which specifies how input is transformed to output; in constrast a machine learning program implements a number of rules specifying how the program should make its own rules for transforming input to output from examples. In situations where the transformation rules are complex (or unknown) the machine learning approach can be very succesfull.

I teach this course together with Christian N. S. Pedersen.

Previous classes

Below are classes that I once used to teach, but am no longer teaching.  Either because someone else is, or because the class is no longer being taught.

Computer science

Applied programming

An introduction to simple script programming of the kind typically used in day to day bioinformatics.fter the course, the participants will have insight into principles and techniques for construction of simple programs for solving known problems in bioinformatics using a common scripting programming language. Furthermore, the participant will have practical experience with implementation of text processing and parsing as well as communication with external programs from within a common scripting programming language. The method of work at the course will also train the participants to plan and complete projects.

Statistics

Mathematical models in systems biology

Biological systems such as cells, regulatory gene networks and protein interaction complexes cannot be understood from reflections on the individual components (genes, mRNA, proteins etc) alone, but must be understood through considerations involving all components at the same time. Naturally, that places heavy demands on the way we perceive the system. Systems biology is concerned with modelling the dynamics of biological systems at a “systems level”, i.e. by considering the interactions of all the components of a system rather than the isolated properties of the components. This course presents mathematical techniques for modelling dynamic systems in this context, with the main focus on stochastic modelling and computer simulation techniques for analysing dynamical systems.

I taught this course together with Carsten Wiuf.

Stochastic models in bioinformatics

Stochastic models are a powerful tool for modelling and analysing many aspects of biology. After this course the participants will have insight into the use of stochastic processes as models for the analysis of genome sequences and other biological problems and knowledge of the needed probability theory background for the handling of such models and practical experience with the programming language R.

I taught this course together with Freddy B. Christiansen.

 Biology

Genome analysis

Genome analysis concenrs analysing the whole genome of a species, or comparative analysis of genomes between several related species, or even the genetic variation within a species at the whole genome level. After the end of the course the students should have detailed knowledge of the existing types of genome data and how the different types of data can be analysed. The method of work at the course will also train the participants to plan and complete in practice analysis of genome data and to present and communicate professional problems.

I taught this course together with Mikkel H. Schierup.


2 thoughts on “Teaching”

  1. Dear Dr.Mailund, I am naive to SNP and analysis, but i wanted to learn the SNP work,data analysis . I seek your help with your tutorials and help of practice sessions online.
    I kinldy request ypou to help me in this regard
    thanking you
    regards
    suresh

  2. I don’t actually work that much with SNP analysis much any longer. In any case, SNP analysis is a huge topic so it is not something I can easily give you pointers to.

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