|Cost||Free of charge|
Due to limited capacities registrations will be pending. Depending on the capacity of tutors we will send out confirmations after the registration deadline until December 22nd latest. Members of the graduate schools BIF-IGS, IHRS Biosoft and HIDSS4Health will be preferred.
The potential applications of ML and Deep learning methods to natural science research are numerous, and include detector development, data analysis techniques and even physical modeling. These techniques will be essential to ensure the highest-quality and -impact scientific results from existing and future experimental works.
This introductory course, held virtually via Zoom, will equip students and young researchers from the community with the basic tools necessary to begin implementing and using ML methods, and will involve some theoretical descriptions but focuses on practical examples of the relevant topics. After this course, learners are able to embark in classification as well as regression projects using scikit-learn or keras.
If you would like to enroll in this workshop, we expect you to bring along knowledge of the python language. The course will be highly interactive and will require you have:
We recommend you to use a local jupyter installation, at best based on anaconda. Details on how to set this up can be obtained from these setup instructions. To prepare for the course, please setup the required packages, start a jupyter notebook and run a cell with the following commands:
import sklearn print('sklearn version: ', sklearn.__version__) import seaborn print('seaborn version: ', seaborn.__version__) import pandas print('pandas version: ', pandas.__version__) from tensorflow import keras print('Keras version: ', keras.__version__)
Please send us your corresponding output, so we can check beforehand if everything works. On Jan 17, 1pm to 3pm, we will offer a zoom call for you to double check your setup.
The videos for the lessons are run on google colab. If you struggle with the installation of anaconda (to use jupyter and the libraries mentioned above), please consider using google colab as a fallback.
The videos of the lessons are shared as video files (not on a video playing platform like youtube or vimeo). Please install a video player before the workshop that can play .mkv files. One good open-source choice is vlc.