Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

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T-61.6060 Special Course in Computer and Information Science VI P:

Data analysis and environmental informatics 5 cr

Lecturers D.Sc.(Tech.) Jaakko Hollmén, Chief research scientist
M.Sc. (Tech.) Mika Sulkava
Assistant M.Sc. (Tech.) Hannes Heikinheimo
Credits (ECTS) 5
Semester Spring 2006 (during periods III and IV)
Seminar sessions On Wednesdays at 14-16 in the room A328 in the computer science building,
Konemiehentie 2, Otaniemi, Espoo. The first session on 18.1.2006.
Language English

Course description

Data analysis aims at identifying general laws about phenomena under observation. In environmental environmatics, the interest is in natural phenomena occurring in our environment. For instance, one could be interested about the nutritional balance of trees as a phenomena, and as a means of investigating, one could observe or measure the nutritional state of trees in different areas and in different times under different climatic conditions.

The course will emphasize the environment as the source of the data and cover data analysis applications for the analysis of environmental phenomena through collected data.

The course will introduce topics of environmental informatics through a collection of published scientific articles. The topics will roughly be divided to three areas: forests, paleoecology, and imaging analysis in remote sensing and radar applications.

The course consists of lectures, and presentations given by students on assigned topics. A data analysis exercise requiring problem solving and programming will be also part of the course.

Course format

Seminar course

Requirements for passing the course

Active participation in the lectures, a seminar presentation and a completed excercise are required to pass the course.


The purpose of the exercise is to solve some data analysis problems in the scope of the course Data analysis and environmental informatics using real-life data. You will think of the solutions to the given problems, implement the solutions, present and discuss the results in a report formatted like a conference paper (max 10 pages all together). In each of the tasks, it is important that you give grounds on the selected methodology and discuss the findings, especially the validated accuracy and the relevance to the original task to be solved.

The deadline of the exercise is 31st of May. Return your report to the course assistant (room B310) in a printed format.

Download the exercise instructions here.
Data sets: Abalone, timeseriesdata.mat and climate_pop_data.mat

A discussion and feedback session for the excercises will be held between 15-17 on thursday 15.6. in room A346.


Time Lecturer Subject
18.1. Jaakko Hollmén
Mika Sulkava
Introduction and kickoff for the seminar
25.1. Rami Rautkorpi Cloud classification in satellite images
1.2. Eemeli Aro Monitoring of northern lights using an all-sky camera
8.2. Jarkko Ylipaavalniemi Vegetation analysis based on remote sensing and neural networks
15.2. Jarkko Tikka
Antti Ukkonen
Input selection and sensitivity analysis of neural networks in hydrology
Machine learning in ecosystem modeling
22.2. Mikko Korpela
Jussi Ahola
Neural networks in air quality forecasting
Organism-based environmental reconstruction with Bayesian techniques
1.3. Mika Sulkava Analysis of forest foliar nutrition data
8.3. - Exam period (No lecture)
15.3. Matthieu Molinier Neural network prediction in lake and marine ecosystems
22.3. Hannes Heikinheimo Clustering of species ranges
29.3. N.N. 10. Session

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