Teaching programme 2008-2009

Autumn 2008: Bioinformatics - Computer science, mathematics and statistics - Biology and medicine
Spring 2009: Bioinformatics - Computer science, mathematics and statistics - Biology and medicine
Language courses
Teaching at departments
Location guide
Previous academic years

Teaching and event calendar

You can find teaching times in the MBI teaching and event calendar.

Autumn 2008

Bioinformatics courses / Autumn 2008

Bayesian paradigm in genetic bioinformatics (6 credits)
Adjunct Professor Sirkka-Liisa Varvio, 17 November - 5 December 2008, Wed 12-16, Thu 14-16, Exactum B120.
Applications of Bayesian approach in computer programs and data analysis of genetic past, phylogenetics, coalescence, relatedness, haplotype structure, disease gene associations. Detailed program schedule during September. Applicable to first and second year studies in Bayesian statistics and Bioinformatics (MBI) Master's degree programmes. Registration: During first lecture or by e-mail to lecturer.

582483 Biological Sequence Analysis (6 credits)
Research Director Esko Ukkonen, 27 October - 2 December, Mon, Tue 14-16 Exactum C221 (II period)
The course covers the basic probabilistic methods for modelling and analysis of biological sequences. Prerequisities: Introduction to Bioinformatics and basics of probability calculus. Course book: Durbin R., Eddy S., Krogh A. and Mitchinson G.: Biological sequence analysis, Cambridge University Press, 1998. Course exam Thu 11th December from 16.00 to 19.00.

T-61.5120 Computational genomics (4-7 credits)
Adjunct Professor Sampsa Hautaniemi, T-building (I-II periods)
Algorithms and models for biological sequences and genomics.

582606 Introduction to Bioinformatics (4 credits)
University Lecturer Esa Pitkänen, 2 September - 10 October, Tue, Fri 14-16 (I period) Exactum C221, Exercises: Tue 16-18 Exactum C221
This course gives an introduction to the central topics in bioinformatics, and gives a foundation for further courses in the Master's Degree Programme in Bioinformatics (MBI). Course book: Deonier R. C., Tavare S., Waterman M. S.: Computational Genome Analysis - An Introduction, Springer, 2005. Course exam Wed 15th of October from 16.00 to 19.00.

Introduction to bioinformatics (Helsinki Summer School course) (4 credits)
University Lecturer Esa Pitkänen, Assistant Professor Sophia Kossida (Academy of Athens), 5-21 August 2008, lectures 10-12 Exactum B222, exercises 13-15 Exactum B221 (summer)

58307312 Master's thesis seminar
University Lecturer Esa Pitkänen, Exactum (I-IV periods)
While working on the Master's thesis, the MBI student is expected to participate in the Master's thesis seminar on a regular basis and give two presentations, one on the research plan and the other on the (nearly) completed thesis. The Master's thesis seminar operates throughout the year.

T-61.5110 Modeling of biological networks (5-7 credits)
Professor Samuel Kaski, 20-24.10.2008, Lectures 10.30-14.30 room 1021, Exercises 14-18 room Y338, T-Building
Models of biological cellular-level networks: gene regulation, signaling, metabolism. Methods for inference of networks from data, and prediction using the models. Mainly probabilistic and machine learning models.

58308302 Seminar: Neuroinformatics (3 credits)
Professor Aapo Hyvärinen 17 September - 8 October, Wed 14-16 Exactum C220, 29 October - 03 December Wed 14-16 Exactum C220 (I-II periods)

58309106 Seminar: Machine Learning in Bioinformatics (3 credits)
Professor Juho Rousu 8 September - 6 October, Mon 12-14 Exactum C221, 27 October - 1 December, Mon 12-14 Exactum C221 (I-II periods)

T-61.5080 Signal processing in neuroinformatics (5 credits)
Adjunct Professor Ricardo Vigario, T-building (I-II periods)
The goal of the seminar is to give an overview of some of the main biomedical signal processing techniques, with clear emphasis to EEG and MEG. There, we should see something about modeling, artifact identification and removal, nonparametric and model-based spectral analysis, segmentation and joint time-frequency analysis. Some closer attention will be given as well to the analysis of event related data. Additional topics include independent component analysis, biorhythms and sleep and synchrony. The material for these extra topics is in the form of several journal papers.

T-61.6080 Special course in bioinformatics II: High-throughput sequencing and its applications (5 credits)
Lecturing Researcher Jarkko Salojärvi, Thu 12-14, T-building A328 (I-II periods)
The purpose of this course is to give postgraduate level knowledge on bioinformatics or a related field. The actual contents of the course vary from year to year. The course can be lectured, or arranged in seminar form.

Statistical methods in genetics (6-8 credits)
Professor Mikko Sillanpää, Tuesday and Thursday 10-12, Exactum B120 (II period)
The course provides an introduction to statistical methods in gene mapping and genetic epidemiology. Basic concepts of linkage and association analysis as well as some concepts of population genetics will be covered. Literature: Duncan C. Thomas: Statistical Methods in Genetic Epidemiology, Oxford University Press (2004). Prerequisites: Basic knowledge of probability calculus and of likelihood based methods in statistical inference. There are no molecular biological prerequisites for the course.

Courses in biology and medicine / Autumn 2008

Genetics courses in Autumn 2008.

Currently the list below contains only the core MBI courses in biology and medicine.

399671 Practical bioinformatics (8-10 credits)
Adjunct Professor Outi Monni, University Lecturer Päivi Onkamo, Biomedicum, Biomedicum (I-IV periods)
Practical Bioinformatics approaches various bio-computational analysis methods essential in life sciences from a practical point of view. The course is tailored for undergraduate and graduate students studying biosciences (biology, biochemistry, medicine etc.) and assumes no previous background in bioinformatics or programming. Basic computer skills are required. The course is organized in Biomedicum Helsinki (Haartmaninkatu 8).

399672 Biology for methodological scientists (8 credits)
Adjunct Professor Outi Monni, University Lecturer Päivi Onkamo, Biomedicum (I-IV periods)
This course gives an introduction to basic concepts and techniques of molecular biology. Particularly, the students will study biological problems that require computational methods to be solved. The course is particularly tailored for students with no previous molecular biology background. The course is given annually.

Courses in computer science, mathematics and statistics / Autumn 2008

Courses listed below are suitable for MBI minor subject studies. Other courses maybe be suitable as well - discuss with your student counsellor!

582630 Algoritmien suunnittelu ja analyysi (4 credits, lectured in Finnish)
Professor Otto Nurmi 3 September - 9 October Wed 12-14, Thu 14-16 Exactum B222 (I period)
Algoritmien yleisiä suunnitteluperiaatteita. Kokoelma keskeisiä ongelmia ja edustavia ratkaisualgoritmeja. Keskimääräisen tapauksen analyysi. Tasoitettu vaativuus. Palautuskaavat. NP-täydellisyys. Esitietovaatimus: Tietorakenteet. Kurssi korvaa vanhojen tutkintovaatimusten mukaisen aineopintojen kurssin Algoritmien suunnittelu. Kurssikoe to 16.10. klo 16-19.

58066 Artificial intelligence (8 credits)
Course is cancelled!

582481 Causal analysis (4-6 credits)
Dr. Patrik Hoyer, 3 September - 10 October, Wed, Fri 10-12 Exactum C221 (I period)
This course probes the main problems of causal analysis: identifying cause and effect, and their use for prediction and decision-making. Prerequisites: Basics of probability theory and linear algebra. Voluntary project work (2 cr) during the second period. Course exam (4 credits) Fri 17th October from 9.00 to 12.00.

T-61.5100 Digital image processing (5 credits)
Jorma Laaksonen (I-II periods)
The main topics of the course are: image enhancement, image restoration, color, spectral analysis and transforms, and image compression.

582632 Diskreetti optimointi (4 credits, lectured in Finnish)
Professor Otto Nurmi 28 October - 4 December Tue, Thu 14-16 Exactum B222 (II period)
Lineaarinen ohjelmointi ja simplex-algoritmi. Kokonaislukuohjelmointi. Verkkoalgoritmit. Heuristiset menetelmät. Esitietovaatimus: Algoritmien suunnittelu ja analyysi. Kurssikoe to 11.12. klo 16-19.

Elementary Bayesian analysis (9 credits)
Adjunct Professor Aki Vehtari, Wednesday 12-16, Exactum D123 (II period)
Elementary Bayesian statistics. Bayesian probability theory and Bayesian inference. Bayesian models and their analysis. Computational methods, Markov chain Monte Carlo. Literature: Gelman, Carlin, Stern & Rubin: Bayesian Data Analysis, Second edition, and other supplementary material announced in the lectures. Prerequisites: basics of probability calculus.

Event history analysis (6-8 credits)
Dr. Kari Auranen and Adjunct Professor Sangita Kulathinal, period I (each day between 01/09/2008 and 16/09/2008, 9:15-13:00).
September 1, 2, 4, 5, 8, 9, 15, 16 Exactum B120; September 3, 10 Exactum C323.
The course gives an introduction to survival and event history analysis, including parametric and non- parametric estimation of survival distributions and the point process formulation of event history models. Data from life sciences are employed as examples. The course includes computer class exercises using the R program. Literature: Andersen, Borgan, Gill & Keiding: Statistical Models Based on Counting Processes. Springer-Verlag, (1993). Kalbfleisch, Prentice: The Statistical Analysis of Failure Time Data. 2nd ed. New York: Wiley, (2002). Prerequisites: basic skills in statistics and programming.

582631 Johdatus koneoppimiseen (4 credits, lectured in Finnish)
Professor Matti Kääriäinen, University Lecturer Marko Salmenkivi 29 November - 5 December Wed, Fri 12-14 Exactum C222 (II period)
Kurssilla tutustutaan koneoppimisen peruskäsitteisiin ja menetelmiin, teoriassa ja käytännössä. Kurssilla käsitellään ohjattua oppimista (luokittelu, regressio) ja ohjaamatonta oppimista (ryvästäminen). Kurssi antaa hyödyllisiä esitietoja useille data-analyysiä ja koneoppimista sivuaville syventäville kursseille eri erikoistumislinjoilla ja bioinformatiikan maisteriohjelmassa. Esitiedot: Ohjelmointitaito ja Johdatus todennäköisyyslaskentaan sekä Lineaarialgebra ja matriisilaskenta I-II (tai vastaavat tiedot). Kurssikoe ke 10.12. klo 16-19.

582216 Johdatus tekoälyyn (4 credits, lectured in Finnish)
University Lecturer Tomi Pasanen, 4 September - 10 November, Thu 10-12, Fri 12-14, Exactum Ck112.

58093 Merkkijonomenetelmät (6 credits, lectured in Finnish)
University Lecturer Juha Kärkkäinen, 2 September - 9 October, Tue, Thu 12-14 Exactum B222 (I period)
Merkkijonohahmon tarkkojen ja likimääräisten esiintymien etsiminen. Merkkijonojen järjestäminen ja hakurakenteet. Tekstin indeksointi. Pakollinen harjoitustyö (työmäärä 1-2 op) jatkuu periodin II aikana itsenäisenä työskentelynä (ei säännöllistä kontaktiopetusta) 5. viikolle asti. Esitietovaatimus: Tietorakenteet ja Laskennan mallit; kurssin Algoritmien suunnittelu tiedoista on hyötyä. Erilliskokeeseen voivat osallistua vain ne, jotka ovat suorittaneet kurssiin kuuluvan harjoitustyön ennen koetta. Kurssikoe ke 15.10. klo 9-12.

T-61.3040 Statistical signal modeling (5 credits, lectured in Finnish, course can be taken in English on request)
Jaakko Peltonen (I-II periods)
The course topic is statistical modeling of data, focusing on linear time-series models. Topics include discrete-time random processes, linear models for signals, Wiener-filtering, introduction to adaptive filtering, power spectrum estimation, maximum likelihood and least-squares estimation

Stochastic modelling (6-8 credits)
Professor Elja Arjas, Period I, Monday 10-12 and Thursday 12-14 in room Exactum B120. Note: First lecture September 18 (I period)
The lecture course is based on the monograph P. Guttorp: Stochastic Modeling of Scientific Data, Chapman and Hall/CRC (1995). The following excerpt from the Preface provides a description of the course: Traditionally, applied probability texts contain a fair amount of probability theory, varying amounts of applications, and no data. Occasionally an author may touch upon how one would go about fitting a model to data, or use data to develop a model, but rarely is this topic given much weight. On the other hand, the few texts on inference for stochastic processes mostly dwell at length upon the interesting twists that occur in statistical theory when data no longer can be assumed iid. But, again, they rarely contain any data. The intent of this text is to present some probability models, some statistics relevant to these models, and some data that illustrate some of the points made. Prerequisites: basics of probability calculus and likelihood-inference, with some knowledge of elementary stochastic processes providing an advantage.

582487 Tiedon tiivistämisen tekniikat (4 credits, lectured in Finnish)
Adjunct Professor Veli Mäkinen 28 October - 4 December Tue, Thu 12-14 Exactum B222 (II period)
Kurssilla tutustutaan tiedon tiivistämisen perustekniikoihin kuten entropiakoodauksiin, Ziv-Lempel koodeihin, PPM sekä Burrows-Wheeler menetelmiin. Painopiste on tehokkaissa tekstin pakkaus-/purkualgoritmeissa. Uutena sovelluskohteena kurssilla käsitellään tietorakenteiden tiivistämistä. Esitietovaatimus: Tekstin indeksointiin liittyvät asiat kurssilta Merkkijonomenetelmät. Kurssikoe ti 9.12. klo 9-12.

Spring 2009

Bioinformatics courses / Spring 2009

57391 Evolution and the theory of games (5 credits)
University Lecturer Stefan Geritz, weeks 3-9, Tue 14-16 B321, Thu 12-14 B322, Exactum (III period)
A game is a mathematical model of a situation of conflict of interests in which the optimal strategy for one player not only depends on his own decisions but also on the decisions of his opponents. The course is an introduction to game theory with emphasis on applications in evolutionary and behavioural biology.

Genome-wide association mapping (6-8 credits)
Adjunct Professor Mikko Sillanpää, weeks 3-9, Mon 10-12 C323, Thu 10-12 B120, Exactum (III period)
The course covers statistical methods and issues involved in association mapping, with a special emphasis on genome- wide level genetic analyses. Replication (verification), confounding factors, and meta-analysis are considered as important themes in the course. Literature: Duncan C. Thomas: Statistical Methods in Genetic Epidemiology, Oxford University Press (2004). Prerequisites: Basic concepts of probability calculus and of likelihood based methods in statistical inference. There are no molecular biological prerequisites for the course. Familiarity with the contents of the course "Statistical Methods in Genetics" is recommended, however.

T-61.5050 High-Throughput Bioinformatics (5-7 credits)
Lecturing Researcher Jarkko Salojärvi, Thu 12-14 T5/A133, T-building (III-IV periods)
The course introduces computational and statistical methods for analyzing modern high-throughput biological data, in particular microarray data, and their use in systems biology. Necessary biological background is reviewed briefly.

T-61.5090 Image Analysis in Neuroinformatics (5 credits)
Adjunct Professor Ricardo Vigario, Wed 12-14 T4 / A238, T-building (III-IV periods)
The goal of the seminar is to give an overview of some of the main biomedical signal processing techniques, with clear emphasis to neural data. Topics range from artifact removal and image enhancement to pattern classification and diagnostic decision.

582605 Metabolic Modeling (4 credits)
Professor Juho Rousu, 10 March - 24 April, Tue, Fri 14-16 Exactum B119 (IV period)
Computational methods in the analysis of metabolic networks, including graph theoretic and stoichiometric approaches, and the analysis of metabolic fluxes. Prerequisites: 582313 Introduction to Bioinformatics, Basics of Linear Algebra. Course exam Wed 29th April from 9.00 to 12.00.

582604 Practical Course in Biodatabases (2-4 credits)
PhD Jarno Tuimala, Adjunct Professor Siru Varvio, 12 January - 17 February, Mon, Tue 14-16 Exactum D122 (III period, practical work in the first half of the IV period)
Techniques for accessing and integrating data in biological databases are studied. The course contains project work. Prerequisities: Introduction to Bioinformatics, basics of databases, basic programming skills.

Phylogenetic data analyses (6-8 credits)
Adjunct Professor Siru Varvio, Monday 15.30-17.00 in Exactum D123, Friday 12-14 in Exactum BK106 (IV period)
The course focus is on phylogenetic inference from DNA sequences by parsimony, maximum likelihood and bayesian methods and program packages. Please note the updated Friday lecture time!

52930 Practical course in protein Analysis (5 credits)
Professor Liisa Holm, 12-30 January 2009, 12.00-17.00 Viikki Infocenter room 170
This course will introduce aspects of bioinformatic analysis of protein sequence and structure, leading to inference of protein function. Participants will be given test sequences to analyse, accompanied by handouts and online help information. Demonstrators will be on hand to assist the participants during practical sessions. The course covers application of a variety of bioinformatics tools in a biological context, and would ideally suit biologists, geneticists, chemists and biochemists interested in integrating bioinformatics techniques into their research. It is also suitable for people from other backgrounds, with some biological knowledge, who are keen to learn about applying computational techniques to biological problem solving. Please note that basic knowledge of proteins, internet usage, and PC skills are required, however, no programming skills are needed or will be used during the course. Registration in WebOodi.

Seminar: Computational systems biology (3 credits)
Adjunct Professor Sampsa Hautaniemi, Mon 13-15 Biomedicum meeting room 1 and 8-9 (III-IV periods)
First meeting on Monday 19 January 2009. The seminar will cover some key areas of systems biology from the computational point of view. Tentative topics: Reconstruction of biological networks, Metabolic engineering at system level, Signal transduction, Protein interaction, Gene regulation, Global properties of biological systems, Integration and visualization of heterogenous data.

Spatial models in ecology and evolution (8 credits)
PhD Eva Kisdi, Weeks 3-9 and 11-18, Tue and Thu 12-14 Exactum B120 (III-IV periods)
This course will explore how to model the dynamics and evolution of populations with spatial movement, spatial constraints and spatial interactions between organisms. We start with a brief introduction to classic mathematical ecology. In spatial ecology, we study diffusion, travelling waves, pattern formation and Turing instability, stochastic patch occupancy models, structured metapopulation models, probabilistic cellular automata and coupled map lattices. Next, we investigate three topical issues of evolutionary biology where spatial structure plays a crucial role: the evolution of mobility (dispersal); the origin of new species via specialisation to different environments; and the evolution of altruistic behaviour. Prerequisites: The basics of analysis, linear algebra and probability theory

T-61.6070 Special course in bioinformatics I (3-7 credits)
Professor Harri Lähdesmäki, Wed 10-12, Thu 10-12, T-building (IV period)
The purpose of this course is to give postgraduate level knowledge on bioinformatics or a related field. The actual contents of the course vary from year to year. The course can be lectured, or arranged in seminar form.

Courses in biology and medicine / Spring 2009

Genetics courses in Spring 2009

Currently the list below contains only the core MBI courses in biology and medicine.

399671 Practical bioinformatics (8-10 credits)
Course continues from Autumn 2008.

399672 Biology for methodological scientists (8 credits)
Course continues from Autumn 2008.

399673 Measurement techniques for bioinformatics (6 credits)
Adjunct Professor Outi Monni, Biomedicum (III-IV periods)
This lab course introduces students to the most fundamental molecular biology technologies. The course is particularly tailored for students with no previous molecular biology background. Prerequisites: 399672 Biology for methodological scientists modules I and II.

Courses in computer science, mathematics and statistics / Spring 2009

Courses listed below are suitable for MBI minor subject studies. Other courses maybe be suitable as well - discuss with your student counsellor!

Computational methods in statistics (6 credits)
Lecturer to be announced (III period)
This course focuses on modern statistical methods which are computationally intensive. The topics include the following: simulation of probability distributions, Monte Carlo integration and importance sampling, the EM algorithm and MCMC methods, which are a group of statistical methods based on the simulation of Markov chains.

582633 Diskreetin optimoinnin harjoitustyö (2 credits, lectured in Finnish)
University Lecturer Juha Kärkkäinen (III period)
Optimointialgoritmin toteuttaminen ja optimointiohjelmiston käyttäminen. Esitiedot: Diskreetti optimointi.

Generalised linear models (5-8 credits)
Doc. Juha Karvanen (IV period)
Generalised linear models are an extension of the usual linear model, where the response variable may be discrete or have a skew distribution. The course covers the generic likelihood-based estimation and test theory of generalised linear models. The most important special cases, like logistic and log-linear models, are treated in more detail.

Hierarchical models (6-8 credits)
Course cancelled!
This course gives an overview of hierarchical modelling and the use of latent variables in model building. Several well- known models can be interpreted as special cases of hierarchical models which are characterised by some dependence structure between the observations. Methods for estimation, model diagnostics and model selection are reviewed and compared. Prerequisites: linear models, generalised linear models.

T-61.5010 Information visualization (5 credits)
Kai Puolamäki (III period)
The course teaches how to visualize information effectively by using the statistical methods, combined with knowledge of the human perception and the basics of data graphics.

Longitudinal data analysis (8 credits)
Lecturer to be announced (III-IV periods)
In longitudinal studies individuals are measured repeatedly through time. It is then possible to separate the age and cohort effects, i.e., distinguish changes within the individual from differences across individuals. The course gives on overview of longitudinal data analysis, including hierarchical models, marginal models and latent process models. Literature: Diggle, Heagerty, Liang, Zeger: Anaylysis of longitudinal data. Oxford University Press (2002). Prerequisites: generalised linear models.

T-61.5140 Machine learning: advanced probabilistic methods (5 credits)
Jaakko Hollmen (III-IV periods)
The course covers probabilistic concepts in machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, computational algorithms for exact and approximate inference, sampling, prior distributions. The course emphasizes understanding fundamental principles and their use in practical machine learning problems.

T-61.3050 Machine learning: basic principles (5 credits)
Kai Puolamäki (III-IV periods)
The topics include the background principles needed to understand and apply the models of machine learning. After the course, the student is able to apply the basic methods to data and understand new models based on these principles.

T-61.3020 Principles of pattern recognition (4 credits, lectured in Finnish, can be taken in English on request)
Erkki Oja (III period)
This course provides basic knowledge on pattern recognition methods and their applications. The course covers the basic concepts of pattern recognition and introduces statistical, syntactical, structural, and neural pattern recognition methods.

582421 Satunnaisalgoritmit (8 credits, lectured in Finnish)
Professor Jyrki Kivinen, 12 January - 18 February Mon, Wed 10-12 Exactum B222, 9 March - 22 April Mon, Wed 10-12 Exactum B222 (III-IV periods)
Satunnaisalgoritmeissa tarvittavia todennäköisyyslaskennan tekniikoita. Satunnaisalgoritmien suunnitteluperiaatteita. Esimerkkejä mm. verkkoteoriasta, tietorakenteista ja laskemisesta. Esitiedot: Algoritmien suunnittelu ja analyysi sekä Johdatus todennäköisyyslaskentaan tai vastaavat tiedot. Kurssikirja: M. Mitzenmacher, E. Upfal. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press 2005. Kurssikokeet ma 23.2. klo 16-19 ja ke 29.4. klo 16-19.

Statistical software tools (3-6 credits)
Lecturer to be announced (IV period)
By the means of exercises and practical work, the students will learn how a statistician can utilize certain software tools. The software packages include the following. The R system for data analysis and graphics, a commercial computer algebra system, the Bugs system for Bayesian analysis of statistical models using MCMC methods, and the LaTeX system for typesetting documents, especially suitable for producing documents containing mathematical formulae.

582634 Tiedon louhinta (4 credits, lectured in Finnish)
University Lecturer Marko Salmenkivi, 11 March - 24 April Wed 12-14, Fri 10-12 Exactum B222 (IV period)
Tiedon louhinnassa tutkitaan usein suuria aineistoja, joista pyritään löytämään uutta, mielenkiintoista ja hyödyllistä tietoa. Kurssi antaa yleiskuvan tiedonlouhintaprosessin eri vaiheista, tyypillisistä tiedonlouhintatehtävistä ja niissä käytetyistä menetelmistä. Kurssin painopiste on toistuvien hahmojen etsinnässä ja satunnaistamismenetelmissä. Esitiedot: Tietorakenteet (tai vastaavat tiedot) sekä ohjelmointitaito. Erilliskokeessa kurssin voi suorittaa myös kirjatenttinä tenttimällä teoksen Tan P., Steinbach M. & Kumar V.: Introduction to Data Mining. Pearson, 2006. Kurssikoe ma 27.4. klo 16-19.

582635 Tiedon louhinnan harjoitustyö (2 credits, lectured in Finnish)
University Lecturer Marko Salmenkivi (IV period intensive phase)
Kurssilla sovelletaan tiedon louhinnan menetelmiä käytäntöön. Opiskelija voi suorittaa opintojakson kahdella tavalla: joko 1) toteuttamalla tehtävänä annetun louhinta-algoritmin ja analysoimalla sillä annettua aineistoa; tai 2) louhimalla tietoa annetusta aineistosta laajemmalla menetelmien kirjolla käyttäen esim. jotakin soveltuvaa valmisohjelmistoa. Kummassakin vaihtoehdossa opiskelija kirjoittaa työskentelynsä tuloksista tutkimusraportin. Esitiedot: Tiedon louhinta.

582636 Todennäköisyysmallit (4 credits, lectured in Finnish
Professor Petri Myllymäki, 13 January - 19 February Tue, Thu 16-18 Exactum B222 (III period)
Johdatus bayesiläiseen mallintamiseen ja data-analyysiin. Kurssilla keskitytään erityisesti monimuuttujamenetelmiin ja Bayes-verkkoihin. Esitietovaatimus: Johdatus koneoppimiseen tai vastaavat tiedot. Kurssikoe pe 27.2. klo 9-12.

582637 Todennäköisyysmallien harjoitustyö (2 credits, lectured in Finnish)
Professor Petri Myllymäki, 12 March - 23 April Thu 16-18 Exactum B222 (IV period)
Harjoitustöissä toteutetaan ja testataan todennäköisyysmallinnuksen menetelmiä, ja tulokset raportoidaan kirjoittamalla tutkielma ja pitämällä posteriesitelmä. Esitiedot: Todennäköisyysmallit.

Unsupervised machine learning (4-6 credits)
Professor Aapo Hyvärinen, 11 March - 24 April 2009 (IV period)
Lectures are on Wednesdays and Fridays, 14:15-15:45 at lecture hall C222. Exercice sessions every week, time and place to be announced.
Unsupervised learning is one of the main streams of machine learning, and closely related to exploratory data analysis and data mining. This course describes some of the main methods in unsupervised learning. In recent years, machine learning has become heavily dependent on statistical theory which is why this course is somewhere on the borderline between statistics and computer science. Emphasis is put both on the statistical (rather Bayesian) formulation of the methods as well as on their computational implementation. The goal is not only to introduce the methods on a theoretical level but also to show how they can be implemented using scientific computing environments such as Matlab or R. Computer projects are an important part of the course. Prerequisites: Basic courses in analysis (including vector analysis), linear algebra I&II, introduction to probability, introduction to statistical inference. (Preferably also some more statistics courses.)

Language courses

993735 Academic Writing for Students in English-Medium Master's Degree Programmes (2 credits)
Autumn 2008
Target programmes: Bioinformatics, Geoinformatics, Atmospheric & Biospheric Sciences & other programmes at the Kumpula campus, plus Forest Ecology & Economy
Teachers: K. Pitkänen & R. Siddall
Place: Exactum (See below)
Times: Fridays 10-14

1 5.9. Group session (Exactum DK116) - KP
2 12.9. Individual consultation (Exactum language teachers' room) - RS
3 19.9. Individual consultation (Exactum language teachers' room) - KP
4 26.9. Individual consultation (Exactum language teachers' room) - RS
5 3.10. Individual consultation (Exactum language teachers' room) - KP
6 10.10. Individual consultation (Exactum language teachers' room) - RS
7 17.10. Group session (Exactum DK116) - KP

993734 Academic Writing for Students in English-Medium Master's Degree Programmes 1 (2 credits)
Spring 2009
Target programmes: Bioinformatics, Geoinformatics, Atmospheric & Biospheric Sciences & other programmes at the Kumpula and Viikki campuses
Teachers: K. Pitkänen & R. Siddall
Place: Exactum, DK118
Times: Fridays 12.15-15.45

Session 1: 16 January (KP)
Session 2: 23 January (RS)
Session 3: 30 January (KP)
Session 4: 6 February (RS)
Session 5: 13 February (KP)
Session 6: 20 February (RS)
Session 7: 27 February (KP)

Academic Writing courses are organized by the Language Support for English-Medium Master's Programmes project.