This course will introduce you to Machine Learning with Python, which is currently one of the most popular topic in data science.
The aim of the course is to teach some of the most important modeling and prediction techniques, along with relevant applications. The following topics will be discussed over the 5 days:
By the end of the week, participants will be able to apply machine learning techniques to real-world data, design and implement their own machine learning applications, and understand both fundamental and advanced machine learning concepts.
Participants should have basic programming knowledge and a strong
motivation for scripting and programming in Python. We expect many of
you to have some experience with Python; for those who do not, please
visit the page how to prepare
Students need to bring their own laptop to do the exercises. Any operating system (Windows, Mac OSX, Linux) is fine. We assume that you have elemental computer skills such as browser usage, storing files, installing programs, etc. Also check that you have full write access and administrator rights to the machine. Some corporate laptops come with limited access for their users, we therefore advise you to bring a personal laptop computer, if you have one.
Course credits of 1.5 EC are offered to students who attend meetings
every day, actively participate in the exercises and participate in the
presentations of the group assignments on the final day of the
course.
No graded activities are included in this course. Therefore, it is not possible to provide students with a transcript of grades. Students will obtain a certificate upon completion of this course.
| Start time | End time | Type |
|---|---|---|
| 09:00 | 10:30 | Lecture |
| Break | ||
| 10:45 | 11:45 | Practical |
| 11:45 | 12:15 | Discussion |
| Lunch at Vening Meinesz building A | ||
| 13:45 | 15:15 | Lecture |
| Break | ||
| 15:30 | 16:30 | Practical |
| 16:30 | 17:00 | Discussion |
Before coming to the first class, it will be useful to familiarize yourself with Google Colab, which is the platform we’ll be using during our sessions. Google Colab provides a cloud-based environment that allows you to write and execute Python code through your browser. It is easily accessible and does not require any local installation.
If you prefer to use PyCharm, you can find an extensive tutorial by JetBrains here. If you prefer to use Jupyter Notebook you can follow a tutorial here.
We look forward to welcoming you all!
Bring a laptop computer to the course and make sure that you have full write access and administrator rights to the machine. Some corporate laptops come with limited access for their users, we therefore advice you to bring a personal laptop computer, if you have one.
Python (named after the British comedy group Monty Python) is one of the most popular programming languages today.
If you would like to refresh your Python knowledge, then you can find many links online to do that. We suggest the following (if you have limited time, then we suggest the first one):
We will be using “An Introduction to Statistical Learning, with Applications in Python” by G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor as the primary reference book for this course. This book covers various statistical learning methods with practical Python labs.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advise all course participants to access the materials online. Lectures are provided in HTML and PDF formats. Practical files contain the exercises, in two versions, with and without solutions. We suggest that you start with the version that has no solutions.
Here you will find the materials for Monday:
Download the data for all the practicals: Data
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advise all course participants to access the materials online. Lectures are provided in HTML and PDF formats. Practical files contain the exercises, in two versions, with and without solutions. We suggest that you start with the version that has no solutions.
Download the data for all the practicals: Data
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advise all course participants to access the materials online. Lectures are provided in HTML and PDF formats. Practical files contain the exercises, in two versions, with and without solutions. We suggest that you start with the version that has no solutions.
We will upload the materials for Wednesday here.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advise all course participants to access the materials online. Lectures are provided in HTML and PDF formats. Practical files contain the exercises, in two versions, with and without solutions. We suggest that you start with the version that has no solutions.
We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advise all course participants to access the materials online. Lectures are provided in HTML and PDF formats. Practical files contain the exercises, in two versions, with and without solutions.
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Fairness and machine learning book by Solon Barocas, Moritz Hardt, Arvind Narayanan Link to the book
On the last day of the course, all the materials will be available in a compact file for download.
We wish all the participants success with their Machine Learning projects!
The course team