The following listing and comments refer to the degree requirement structure of MBI at University of Helsinki. MBI students at Aalto University should consult their student councellor on mapping the courses to the corresponding modules.

Bioinformatics courses / Spring 2011 (preliminary)

This list includes the major courses. Note that some of them can also be included as minor subject studies in Computer science, Statistics, Biology.

period III

T-61.5050 High-Throughput Bioinformatics (5-7 cr), periods III-IV
PhD Jarkko Salojärvi. Tue 10-12 Exercises Tue 13-14
Placement: 1st year

57729 Phylogeny inference and data-analysis (4-10 cr), periods III-IV
University Lecturer Sirkka-Liisa Varvio. Tue 15-17 and Thu 14-16 in III period, lectures and assignments (4-6cr). In IV period practical work in computer computer class with negotiable time schedule (4-6cr). Note that it is not necessary to take IV-period content in order to get credits from the III-period. Placement 1st or 2nd year.

582653 Computational Methods of Systems Biology (4 cr), period III
University Lecturer Juho Rousu 17.01.-24.02. Mon 12-14, Thu 10-12 B222
Placement: 1st or 2nd year

period IV

582483 Biological Sequence Analysis (4 cr), period IV
Professor Veli Mäkinen 14.03.-27.04. Mon, Wed 10-12 D122
The course covers the basic probabilistic methods for modelling and analysis of biological sequences. Prerequisities: Elements of Bioinformatics. Course book: Durbin R., Eddy S., Krogh A. and Mitchinson G.: Biological sequence analysis, Cambridge University Press, 1998.
Placement: 1st or 2nd year

Seminars

Courses in biosciences  / Spring 2011

xxxxx High-throughput sequencing (2 cr), period III

Full days 19.01 and 20.01 in Viikki Infobuilding (Corona) Viikinkaari 11, Room 170 and sequencing laboratory, from 9.00 to ~16.00. Exam 26.01, 15.00 in CULT 2 building. Petri Auvinen and Lars Paulin.

850012 Gene Technology (3 cr), period IV

Full days 04.04 - 12.04, 9.00-17.00, Viikki, h178, D-building. Registration: Weboodi, code 850012. Kristiina Mäkinen.

Links to other biology courses:

• Department of Biological and Environmental Sciences (Viikki)
• Faculty of Agriculture and Forestry (Viikki)
• Faculty of Pharmacy (Viikki)
• Curriculum 2010-2011, Master's Degree Programme in Biotechnology
• Curriculum 2010-2011, Master's Degree Programme in Plant Production Science

Courses in computer science, mathematics and statistics / Spring 2011 (preliminary)

Courses listed below are suitable for MBI minor subject studies. Other courses may be suitable as well. Note that some of the listed courses can also be included as major subject studies in bioinformatics (if in accordance with an approved personal study plan). Please see this page for links to more complete course lists at individual departments.

period III 

57733 Computational statistics (10 cr), periods III-IV
University Lecturer Petri Koistinen. Mon and Fri 12-14, B120.
Computational methods which are useful especially in Bayesian statistics, methods for generating independent samples from distributions, Monte Carlo integration and importance sampling, approximating the posterior distribution using numerical quadrature or Laplace expansion. MCMC methods and theory, Gibbs and Metropolis-Hastings sampling, EM algorithm, multi-model inference.

57710 Software tools for statisticians (3-6 cr), period III
University Lecturer Petri Koistinen, Mon and Thu 10-12, computer class C128

57745 WinBUGS/OpenBUGS with applications, period III
Adjunct professor Jukka Ranta, Mon 16-18 C124, Thu 16-18 B120
The program packages which are widey used tools in stochastic Bayesian statistics. The applications in risk estimation in food production, mikrobiology, veterinary diagnostics adn epidemiology. Among the main goals are analysing hierarchical probability models simulation results. Prerequisites: Basics in Bayesian inference.

582636 Probabilistic Models (4 cr), period III
University Lecturer Huizhen Yu 18.01.-24.02. Tue, Thu 16-18 B222
This course provides an introduction to probabilistic modeling with emphasis on graphical models and their applications in artificial intelligence, machine learning, and data mining.

582668 Project in String Processing Algorithms (2 cr), period III
University Researcher Juha Kärkkäinen 18.01.-22.02. Tue 12-14 B119
Implementation and experimental comparison of string processing algorithms.

582669 Supervised Machine Learning (4 cr), period III
Professor Jyrki Kivinen 18.01.-24.02. Tue, Thu 10-12 C222
We study classification from the point of view of so-called statistical learning theory.

T-61.5090 Image Analysis in Neuroinformatics, periods III-IV

PhD Ricardo Vigario.  Wed 12-14, Exercises ?

T-61.5010 Information visualization (5 cr), period III
Amaury Lendasse & Francesco Corona, Wed 10-12, Thu 10-12, Exercises Tue 10-12 & 14-16

T-61.5140 Machine Learning: Advanced Probabilistic Methods (5 cr), periods III-IV
Jaakko Hollmén. Fri 10-12, Exercises Tue 16-18

period IV

57744 Bayesian theory with applications, period IV
Professor Jukka Corander. Tue and Thu 12-14, B120.
The aims of the course are to decipher the Bayesian machinery, how and why it works, as well as to gain detailed understanding of an array of its applications. Prerequisites: Probability calculus, calculus, linear algebra. Stochastic processes and computational statistics are useful, but not obligatory.

582634 Data Mining (4 cr), period IV
Professor Hannu Toivonen 14.03.-28.04. Mon, Thu 9-12 B222
The course covers the data mining process, typical data mining tasks, and central data mining methods, with emphasis on discovery of frequent patterns.

582638 Unsupervised Machine Learning (4 cr), period IV
Professor Aapo Hyvärinen 15.03.-29.04. Tue, Thu, Fri 14-16 C222
Unsupervised learning is one of the main streams of machine learning, and closely related to multivariate statistics and data mining. This course describes some of the main methods in unsupervised learning.

582637 Project in Probabilistic Models (2 cr), period IV
Professor Petri Myllymäki 17.03.-28.04. Thu 16-18 C220
The task in this course is to implement and empirically validate probabilistic modeling techniques on a real-world data analysis problem.

582674 Projects in Unsupervised Machine Learning (3 cr), period IV
PhD Michael Gutmann
Practical implementation of methods taught in the course Unsupervised Machine Learning.

582635 Data Mining Project (2 cr), period IV+
Professor Hannu Toivonen 03.05. Tue 10-12 B222, 20.05. Fri 10-12 B222
Application of data mining to a data analysis problem.

Language courses at University of Helsinki (preliminary)