Registration of interest for courses on PhD level 2018

OBS! This page is obsolete and only kept as information. The following courses are offered during 2018:

Suggested courses to give during 2018

Optional courses 2018

Decision making and aggregation, 3,5 credits, spring 2018
This course will give an introduction to methods for data aggregation. We will discuss methods for data aggregation beyond the usual arithmetic and weighted mean. We will discuss their use in the context of multicriteria and multiobjective decision making, as well as in information fusion. Instructor:

Data privacy, 5 credits, spring 2018
This course will give an overview of the problems related to data privacy. The course will include topics related to user, holder and respondent privacy. Methods and tools from the areas of statistical disclosure control, privacy enhancing technologies and privacy preserving data mining will be presented.

Fuzzy sets and systems, 3,5 credits, spring 2018
This course will give an introduction to fuzzy sets and systems in the context of approximate reasoning. We will introduce fuzzy sets, and then present some of the tools that use fuzzy sets. We will discuss, among others, fuzzy systems, fuzzy clustering and fuzzy optimization.

Evaluating the applicability of research results in new domains using demonstrators, 5 credits, autumn or spring 2018
This course is suitable for PhD students with a focus on both informatics or engineering science. During the course the students will design and implement their own research demonstrator. The demonstrator should be based on the student's own research, but the application scenario used in the demonstrator should be chosen from another domain than the one the student is usually working within. The purpose is to demonstrate the broader applicability of the research and to practically evaluate the research in a new domain.

Reviewing and writing a scientific contribution, 5 credits, spring 2018
Students receive training in writing scientific publications that follow academic writing standards and convey the most important scientific results and conclusions in an engaging way. Writing expertise is developed through:

• A few instructions on best practice in how to write a academically sound and communicatively attractive paper/article (lectures and individual study)
• Collaboratively reviewing and evaluating strengths (and weaknesses) of published articles in leading journals and conferences (individuals study and seminars)
• Writing an own paper and getting feedback on various draft versions (individual study and supervision meetings) With regard to the last bullet point, PhD students are expected to use "research work in progress" and "draft publications" related to their own PhD project in the course. These drafts will be further developed during the course. PhD students will get feedback from a researcher not directly involved in their own PhD project. The final (to be submitted) version will be part of the examination for this course.

Qualitative research, 3 credits, spring or autumn 2018
The course gives an in-depth introduction to qualitative research, its design and execution. It covers the following topics:
1. Qualitative research paradigms
2. Ethical aspects
3. Sampling
4. Types of qualitative data collection
5. Common mistakes during data collection
6. Transcription methods
7. Data analysis
8. Quality aspects of qualitative research
9. Publishing qualitative research

Conceptual modeling and metamodeling, 3 credits, autumn or spring 2018
The course discusses how inter-related domain-specific modeling languages (DSML) can be designed and then used to model a given domain, such as computer networks, business processes, information flows, etc. The course provides the theory in form of lecture material, a textbook, and a number of tutorials. The course addresses PhD students that are in need for a modeling language that has currently no tool implementation. The emphasis is on domains that typically have large models that cannot easily be analyzed by manual inspection. The course uses the tool for the practical parts. The course is given in the form of seminars and tutorials. The course is evaluated on the basis of the DSMLs and models produced by the students. Knowledge of logic and/or database query languages is useful.

Quantitative and statistical methods, 3 credits, autumn 2018
The course covers quantitative methods and statistical analysis, for example analytical statistics, hypothesis testing, parametric and non-parametric analysis, etc.

The student will learn to:
* Evaluate methods and statistical approaches in science.
* Select appropriate statistical method in their research.
* Organise and structure data sets appropriately.
* Work with statistical methods and interpret the results.
* Use statistics software and tools, e.g. SPSS.
Instructor: to be announced later

Compulsory courses 2018 (for PhD students in informatics)



Registration of interest for courses on PhD level 2018


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