Hands-on Workshop: Exploring Research Data with Artificial Intelligence and Design Thinking
Reportedly, when applying artificial intelligence in various domains, the organization of data requires up to 80 % of the time. This full day workshop aims at introducing participants to the diverse tasks in data organization by employing Design Thinking.
Time and place
January 11th 2019, 09:00-16:00, Georg Sverdrups hus, room Linken
This workshop is fully booked.
The workshop is structured in 4 sessions of 60 to 120 minutes each.
- Session 1: Data organization tasks (e.g., finding and acquiring data, cleaning the data, filtering the data, etc.) are introduced and participants will experiment with them on small and simple exercises in Python. (90-120 min)
- Session 2: Design Thinking is introduced as a methodology to analyze a given problem and to develop solutions in a team. Each team uses Design Thinking to explore a research data set provided by the organizers. The organizer will provide also access to tutors covering each subject (design, data-scientist, and so on). For all roles no a-priori knowledge is required. (120 min)
- Session 3: A brief introduction to Machine Learning will be provided. Teams will implement their solution approaches by primarily working with the data (reusing data organization tasks they learned in Session 1). The data will be used to learn models with learners prepared in advance by the organizers, hence the participants can only impact the results by working on the data. (120 min)
- Session 4: Each team shall give a short presentation about the solution they have developed, how they organized the work, and challenges they mastered. (60 min)
The necessary infrastructure preferably Jupyter notebooks, raw data sets, model training, validation and inference infrastructure for the hands-on work will be provided by the organizers. Participants only need to bring their own laptop with a browser.
Basic knowledge of the Python programming language is required.
Researchers and UiO staff interested in working with artificial intelligence/machine learning/deep learning.
Andrea Gasparini (Oslo University Library), André Walsøe (Oslo University Libarary) Thomas Röblitz (USIT)