Zoom link for the introduction:
https://desy.zoom.us/j/69167728936?pwd=aDVpOTRDdFJKY05kRGRORTgvQVRsZz09
Meeting ID: 691 6772 8936
Passcode: 165916
The school aims at PhD students and young postdocs, and offers a basic introduction to machine learning. Specifically, three main topics will be discussed:
- The predictive modeling pipeline
- Selecting the best model
- Evaluating model performance
In addition to the lectures, several hands-on tutorials based on Scikit Learn will be offered.
In order to participate successfully in the school, we expect the following prerequisites:
- Basic knowledge of Python programming : defining variables, writing functions, importing modules.
Some prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required.
For a quick introduction on these requirements, you can go through these course materials or use the following resources:
Course Modalities
The course will take place every morning from 9am to 12.30pm. All learners will split into teams of about 10 people and received close mentoring by a experienced Machine Learning user and practitioner.
More details about the course including required software environment is available on this note pad. In addition to this, we invite all learners to enter our mattermost chat for the course. Please use this invite link to get into the teachml team!
To get started with the notebooks, please download the learner pack (you find the zip archive in the materials section of this event) and unzip it to your hard drive. The zip archive contains datasets, notebooks and installation instructions to get you started. Please install all required software until the start of the course!
The school is supported by the Excellence Cluster Quantum Universe Hamburg.
