DISA

It is a pleasure to invite you to the third Baladria Summer School in Digital Humanities, this year awarding credits and taking place both online and in Zadar. The schedule is as follows:

• First week, 12-18 June – online, asynchronously (obligatory online participation)
• Second week, 19-23 June – Zadar, Croatia (obligatory in-person participation)
• Last weeks, 24 June to 05 Juley — online, asynchronously (obligatory online participation)

Application opens: 17 February 2023

Application closes: 15 March 2023

For more information and to apply, please visit the course website at https://lnu.se/en/course/methods-for-digital-humanities-baladria-summer-school-in-digital-humanities/vaxjo-distance-international-summer/.

Welcome!

The findings, experiences, and ideas that emerge from research have traditionally been utilized through academic publication and teaching programmes. However, academic impact alone is no longer enough for a successful research career. With the growing emphasis in the research funding landscape on maximizing impact beyond academia, it is increasingly important that researchers reach wider society by embedding non-academic impact strategies in their projects, by working with a range of non-academic partners, and by using ever more innovative methods of dissemination and utilization. This course showcases a range of approaches researchers can employ to ensure that their research has impact and relevance beyond universities. It will also provide students with tools that will help them best communicate the value of their work to research funders and potential investors.

How to Apply?
Opens 2nd March at 9:00 AM. Please send your application to fyrklovern.doktorand@kau.se by 11th May. You should provide your name, department, contact details, and a short description (max. 100 words) of your research project. Please ensure that you obtain your supervisor’s approval for attending the course, and also state their name in your application email.

Eligibility and further details
The course is offered to PhD students in all disciplines from Karlstad University, Linnaeus University, Mid Sweden University and Örebro University. The course consists of three mandatory sessions in the autumnof 2020 (at Karlstad, Sundsvall, and Stockholm). The course syllabus has been evaluated and approved by the Research Education Committee at Karlstad University’s Faculty of Humanities and Social Sciences. The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

The syllabus at https://libra.sae.kau.se/course/att-nyttiggora-forskningoch-vetenskapinnovative-applications-research-andscience

For more information contact: Martina Lago, Innovation Advisor, martina.lago@lnu.se or 0480-446062
PhD-course 2020

The main objective with this course is to get an introduction into modern statistical methods for modeling and and prediction of data. After successfully completing the course, the student is anticipated to be able to

• Demonstrate a conceptual understanding of the following fields in statistics: classification, resampling methods, linear model selection and regularization, and unsupervised learning.
• Apply modern statistical software for classification, resampling methods, linear model selection and regularization, and unsupervised learning.

The course contains

• Linear regression: simple and multiple linear regression with assessing the accuracy of the coefficients and the model and comparison with K-nearest neighbors
• Classification: logistic regression, linear discriminant analysis, K-nearest neighbors
• Resampling methods: cross-validation, bootstrap
• Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions
• Unsupervised learning: Principal component analysis, clustering methods
• Writing and presentation of a report where real data materials are analyzed with appropriate statistical approaches from the particular statistical field

Type of Instruction
Teaching consists of lectures, presentations, laboratory work, and tutoring.

Examination
The course is assessed with the grades A, B, C, D, E, Fx or F. The grade A constitutes the highest grade on the scale and the remaining grades follow in descending order where the grade E is the lowest grade on the scale that will result in a pass. The grade F means that the student’s performance is assessed as fail (i.e. received the grade F). The student’s knowledge is assessed in form of

• Graded conceptual assignments (3 credits), grades A to F
• Graded computer assignments (3 credits), grades A to F
• Presentation of the use of statistics in the student’s research alternatively presentation of a statistical topic not covered in this course (1,5 credits), grading scale U-G.

Required reading
G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning: with applications in R, latest edition, Springer.
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, latest edition.

Timetable
The course will start on Fri March 27 and finish by the end of May/beginning of June. If convenient for participants it is suggested that we meet weekly on Fridays at, say 13:15.

Registration
Try to finalize the registration no later than Mar 17 so it will be easier to plan. Register here https://forms.gle/a3zzgG5toouMFqYb7

Prerequisites
1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.

If you have any questions please contact Roger Pettersson (https://lnu.se/personal/roger.pettersson/).

Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course mixes theory and practice, with a focus on applied machine learning where we learn what algorithms and approaches to apply on different types of data.

The course includes the following:
• Supervised learning, different types of data and data processing
• Algorithms for handling text documents
• Algorithms for handling data with numerical and categorical attributes
• Neural Networks
• Deep Learning for image recognition

The course will start on Tuesday October 8th with workshops on October 29th, November 26th
The registration needs to be finalized no later than September 19th 2018 via this link https://forms.gle/Qgk91hk7tTxzp7rs7

If you have any questions please turn to Johan Hagelbäck – johan.hagelback@lnu.se

The findings, experiences, and ideas that emerge from research have traditionally been utilised through academic publication and teaching programmes. However, academic impact alone is no longer enough for a successful research career. With the growing emphasis in the research funding landscape on maximising impact beyond academia, it is increasingly important that
researchers reach wider society by embedding non-academic impact strategies in their projects, by working with a range of non-academic partners, and by using ever more innovative methods of dissemination and utilisation. This course showcases a range of approaches researchers can employ to ensure that their research has impact and relevance beyond universities. It will also provide students with tools that will help them best communicate the value of their work to research funders and potential investors.

Lecturers and content

You will be provided with knowledge around idea development within research. Particular emphasis will be given to developing an understanding of ’research impact’ and how to embed this in your projects.

Eligibility and further details

The course is offered to PhD students in all disciplines from Karlstad University, Linnaeus University, Mid Sweden University and Örebro University. The course consists of three mandatory seminars in the autumn of 2019 (Karlstad Sep 11-12th, Kalmar Oct 22-24th and Stockholm Nov 21st). The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

How to Apply?
Please send your application to fyrklovern.doktorand@kau.se by 11th May. You should provide your name, department, contact details, and a short description (max. 100 words) of your research project. Please ensure that you obtain your supervisor’s approval for attending the course, and also state their name in your application email.

For more information please contact: Mathias Gaunitz – mathias.gaunitz@lnu.se or 0470-70 89 79

We are not offering you a second chance to take the PhD-course in Applied Machine Learning this fall.

Course content:

Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course mixes theory and practice, with a focus on applied machine learning where we learn what algorithms and approaches to apply on different types of data.

The course includes the following:

• Supervised learning, different types of data and data processing
• Algorithms for handling text documents
• Algorithms for handling data with numerical and categorical attributes
• Neural Networks
• Deep Learning for image recognition

Timetable

The course will start on Tuesday October 9th and finish by the end of the semester.

Registration

The registration needs to be finalized no later than September 19th 2018

Register here: https://goo.gl/forms/jn1DAAQsb5zm8S1D3

If you have any questions please turn to Johan Hagelbäck – johan.hagelback@lnu.se