Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
17.9.2019 at 11:00 - 29.10.2019 at 15:00

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Wed 30.10.2019
14:00 - 16:00
Thu 28.11.2019
10:00 - 12:00
Mon 16.12.2019
12:00 - 16:00

Description

After the course the participant can:

● Explain what Bayesian Networks are and how they work

● Evaluate theoretical, scientific, and cognitive factors that need to be taken into account when designing an interdisciplinary BN model

● Design and build an interdisciplinary Bayesian Network model on their research question using a readily available software package

● Find and evaluate information sources to populate the model

Week

Topics Learning modes
1

Introduction

Interdisciplinary research

Problem framing

Introduction to Bayesian Netowrks and decision support models

How are Bayesian Networks useful for interdisciplinary environmental research

First meeting face-to-face: introductions, division to groups, first lectures

Articles to be read

Video lectures

Video material from YouTube

Group discussions in Moodle

2

Theory of Bayesian Networks

How to build the models in BN software

Articles to read

Video lectures

Hugin exercises (getting to know Hugin)

Peer support + Q&A in Moodle

3 Building the model: structure and parameters

Articles to read

Video lectures

Example Hugin models for exploration

Moodle discussion in groups

4 Building the own models in groups with Hugin software

Articles to read

Previous lectures and materials

Independent and group work

Discussions in Moodle groups (1 face to face meeting), peer learning and collaboration

5 Working on own models

Problem solving clinic with the teacher (2h)

Work in groups, Q and A in Moodle.

6

Working on own models

Preparing a presentation of the model

Work in groups, Q and A in Moodle.

Guidelines for writing an abstract and making a conference presentation.

7

Presentations of the models in the course conference

Peer evaluation of the projects

Guidelines for peer review

For the first 3 weeks, there will be 3-4 video lectures, 2-4 scientific articles, and some other material (videos from YouTube, exercise sheets) every week. For weeks 4-6, the focus will be on work on individual models - the work will be done in interaction with the sub-group, but everyone will have their own project. There will be some articles to read, but fewer than in weeks 1-3. In week 7, the students will present their own models (problem framing, interdisciplinary aspects, model structure and why it was selected, data sources, critical assessment) in the course’s mini-conference.

The course extent is 5 credit units, and is taught over one (7 week) teaching period. Grading is Pass/Fail.

Requirements for passing:

● Active participation in group discussions

● Active participation in peer collaboration

● Presentation of own model in final conference

● Peer evaluation of two presentations

According to the course content.

Pass/Fail

Teachers:

Dr Laura Uusitalo is docent of aquatic sciences at the University of Helsinki. She has a PhD in fisheries science and MSc degrees both in limnology and computer science. She works as a leading researcher at the Finnish Environment Institute. She has over 18 years of experience on Bayesian Networks, and has taught several workshops on them. She is responsible for the scientific content of the course.

Dr Riikka Puntila has a PhD in aquatic sciences and MSc in marine sciences. She works as a researcher at the Finnish Environment Institute. She has designed the pedagogical approaches of the course.

MSc Susanna Jernberg is working on her PhD thesis in environmental sciences at the University of Helsinki. She has a MSc in environmental sciences. She works as a researcher at the Finnish Environment Institute, and uses Bayesian Networks in her work.