We are pleased to announce a four-day course to give a hands-on introduction to the field of machine learning using Python. We plan to hold this course on site at ETH Zurich (OCT) on Thu 8th, Fri 9th, Thu 15th and Fri 16th September 2022. In case ETH Zurich reintroduces COVID-19 safety regulations, the course will be held online.
The course will be given by Dr. Tarun Chadha, Dr. Franziska Oschmann, Dr. Uwe Schmitt and Dr. Manuel Weberndorfer from the Scientific IT Services.
The aim of the course is to provide a robust understanding of the basic concepts of machine learning (focusing on supervised learning) to people with no prior experience in machine learning. During the course the basic concepts of machine learning are introduced by using as little math as possible. Hands-on sessions will allow the users to learn how to use Python for applications of both classical machine learning as well as neural networks methods.
This course is not:
- a comprehensive introduction into the field;
- focusing only on deep learning;
- an introduction to Python.
Preliminary course layout
Thursday, September 8th:
- General introduction
- Concepts of classification
- Overfitting and cross-validation
- How to assess the quality of a classifier
Friday, September 9th:
- Processing-pipelines and hyperparameter optimisation with scikit-learn
- Overview of basic classification algorithms
Thursday, September 15th:
- Concepts of regression
- Introduction to neural networks and deep learning with TensorFlow
- After the course: Apero
Friday, September 16th:
- Introduction to neural networks and deep learning with TensorFlow (continued)
On all four days we start the lectures/hands-on sessions at 9:30am and expect to end around 3:30pm.
At every course day we will offer an additional 1 hour of data science consultancy where participants can discuss their individual data science problems, questions and projects with the tutors.
The consultancy sessions take place after the official course hours from 4 to 5pm and are voluntary.
We invite all participants to an Apero after the course on the 15th of September. We would like to take this opportunity for personal and professional exchange and encourage all participants to join.
You need to have some basic understanding of Python. No working experience of machine learning is expected.
Participation and registration fees
This course is open to the following members of ETH Zurich: PhD students, postdocs, scientific/technical staff, professors. Bachelor/master students are not eligible to participate in this course. The maximal number of participants is 30, filled on a "first come, first served" basis. Surplus registrations will form a waiting list.
The registration fee for the course is 400CHF. Please note that we only accept credit card payments.
Important note: For SIS subscription customers this course is free of charge; in this case the course fee is just a deposit and will be refunded to the participant after the course if he/she has attended the full course. If you are unsure, please check with your PI whether your group/department has a SIS subscription. Please note that being a member of an HPC shareholdership on Euler/Leonhard or making use of our RDM/openBIS service alone do not suffice for free participation.
To apply for the course please fill out the application form.