Plans week 46, November 14-18
Dear all, welcome to a new week with FYS-STK.
Last week we wrapped up our discussions on decision trees and ensemble methods based on decisions trees (bagging, random forests, boosting and gradient boosting). These are all very popular methods and in particular for classification problems, often produce excellent results on training and predictions. And they are all simple to implement and have a low-level of mathematical complexity. Last week we started also with our last supervised learning method, Support Vector machines. This topic will also keep us busy this coming week. We are also planning to run an eventual mini-workshop on possible topics for project 3. Here you'll find the topics presented by different groups in 2020 and 2021.
In 2020 the contributions were (and some of these ended up in thesis work and/or publications, online only due to Covid-19!)
- Maria Emine Nylund: Lego Bricks Classifier
- Fabio Rodrigues Pereira: Financial Machine Learning
- Markus Borud Pettersen: Machine Learning and Brain Grid Cells
- Jing Sun and Endrias Getachew Asgedom: Machine learning-based approaches to denoising microseismic data
- Felicia Jacobsen: Analysis of Breast Cancer Data
- Simon Elias Schrader: Predicting atomization energies of molecules
- Varvara Bazilova and Sergio Andres Diaz Mesa: Glacier Mapping and Machine Learning
- Gert Werner Kluge, Hanna Alida Fossen Hardersen and Sushma Sharma Adhikari: Gamma ray signals stemming from dark matter in the galactic center
Workshop topics 2021 (partly online)
- 1215-1225pm: Are Frode Helvig Kvanum, Gard H?ivang, and David Andreas Bordvik, Next-day forecasts on spot prices for electricity
- 1225-1235pm: Lidia Luque, Voxel-wise multi-label brain tumor classification
- 1235-1245pm: Marcus Berget et al, Locating suspicious brain activity using neural networks
- 1245-1255pm: William Ho and Tom-Ruben Traavik Kvalvaag, Comparing semi-supervised learning and supervised learning for image classification
We would very much like to organize something similar this year as well. Feel free to come with suggesstions by Thursday November 17. We will then try to set up the various contributions for Friday November 18. The presentations last normally 5-15 mins and span from loose ideas to more well-defined topics. Nothing pretentious, we wish to keep this as low-key as possible.
Else, the teaching material for this coming week is at the slides for week 46, see https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46.html.
We recommend also chapter 12 of Hastie et al and sections 7.1 and 7.2 of Bishop.
For those of you wanting to read more on Support Vector Machines, there are many good texts, and one is at
https://link.springer.com/book/10.1007/978-0-387-77242-4. You can download this for free via a UiO IP number.
Project 3 is available from Sunday November 13 at 10pm.
Else, our last week is November 21-25 and then we will try to summarize the various supervised learning methods we have covered and also discuss unsupervised methods like Clustering and PCA. And that ends our semester.
Best wishes for the coming week,
Morten et al