Compulsory group assignments MA8701V2023

Go to the Data analysis compulsory group project page

• G. Wilson, J. Bryan, K. Cranston, J. Kitzes, L. Nederbragt, T. K. Teal (2017). Good enough practices in scientific computing. PLOS computational biology. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510
• J. Bryan. Happy Git and GitHub for the useR. https://happygitwithr.com/
• Lang T, Altman D. (2013). Basic statistical reporting for articles published in clinical medical journals: the SAMPL Guidelines. In: Smart P, Maisonneuve H, Polderman A (eds). Science Editors’ Handbook, European Association of Science Editors https://www.equator-network.org/wp-content/uploads/2013/07/SAMPL-Guidelines-6-27-13.pdf

• Kaggle https://www.kaggle.com/datasets
• UCI Machine Learning Repo https://archive.ics.uci.edu/ml/datasets.php and new page (beta) https://archive-beta.ics.uci.edu/
• Google dataset search: https://datasetsearch.research.google.com/
• Felles datakatalog: https://data.norge.no/
• World bank open data: https://data.worldbank.org/
• The humanitarian data exchange: https://data.humdata.org/
• Gapminder and R-package
• Tidy tuesday

• Gastrointestinal Lesions https://archive.ics.uci.edu/ml/datasets/Gastrointestinal+Lesions+in+Regular+Colonoscopy
• https://www.kaggle.com/uciml/breast-cancer-wisconsin-data?fbclid=IwAR0jWt9beLgsiiKO1lm8slBG08q4pc_LMkbnZuZKDhqFfKI330SQBkj-zk0&select=data.csv
• https://www.kaggle.com/mirichoi0218/insurance
• https://www.kaggle.com/kiranraje/prediction-facebook-comment
• https://data.ub.uni-muenchen.de/2/1/miete03.asc
• https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
• Flomdata from NVE, predict water flow at Gaulafossen and/or Eggafossen (data from NVE)
• https://www.kaggle.com/aungpyaeap/fish-market
• https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

Some (open) git-repos for the group work project report in 2021:

• https://github.com/dozzedoff/MA8701/tree/main/prosjekt1
• https://github.com/GunGro/MA8701
• https://github.com/ebbeberge/water-flow-modeling
• https://github.com/Vimlekisen/MA8701_project1

Go to the Group article presentation page

In 2021 there were 9 groups - see overview for articles that were presented. The main topics were:

• Ethics of AI - 3 groups (gender bias in image classification, survey of bias and fairness, predictin parole COMPAS)
• Design of experiments and response surface methodology to tune machine learning hyperparameters
• Two cultures: statistics vs data science/machine learning - 2 groups
• Neural networks to inspect wind turbine rotor blades (research field of participants)
• Weight uncertainty in neural networks (research field of participants)
• Introduction to reinforcement learning (reasearch field of participants)