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Two day course: Introduction to Machine Learning using Python

Dear members of DBIOL and DGESS,

we are pleased to announce a two day course to introduce members of DBIOL and DGESS to the field of machine learning.

The course will take place on 6th and 7th of May 2019 in HG D 12.

The course targets members of DBIOL and DGESS who have no experience in machine learning.

Description

Our goals are:

  • to provide a robust understanding of the basic concepts of machine learning, especially classification.
  • to introduce these concepts using as little math as possible.
  • to teach how to use Python for machine learning, especially the libraries scikit-learn and keras.

This course is not:

  • a comprehensive introduction into the field,
  • focused on deep learning,
  • an introduction to Python for general data science.

Requirements

This course might be right for you if you can answer the following questions with yes:

  • I have a basic understanding of Python
  • I have no working experience with machine learning
  • I am a member of DBIOL or DGESS

Preliminary course layout

Monday, May 6th

  1. General Introduction
  2. Concepts of Classification
  3. Overfitting and cross-validation
  4. How to assess the quality of a classifier

Tuesday, May 7th

  1. Overview over important classification algorithms
  2. Concepts of Regression
  3. Processing-pipelines and hyperparameter optimization with scikit-learn
  4. Introduction to neural networks and deep learning with keras

Organizational aspects

The course will take place on 6th and 7th of May 2019 in HG D 12 and is limited to 25 participants.

We start at 9:30 and expect to end around 17:30 on both days.

The course room is equipped with prepared computers.

To apply for the course please fill out this form

Your tutors will be:

  • Dr. Mikolaj Rybinski
  • Dr. Tarun Chadha
  • Dr. Uwe Schmitt

For further questions don’t hesitate to contact Tarun Chadha or Uwe Schmitt.

Kind Regards

Uwe Schmitt

———————-
uwe.schmitt@id.ethz.ch
Scientific IT Services
ETH Zurich
https://sis.id.ethz.ch
https://twitter.com/eth_sis