Machine learning is a crucial component of the growing field of data science. Join our course to learn how we can uncover hidden insights in data and how algorithms learn to make classifications or predictions.
Artificial intelligence and Machine Learning have revolutionized various aspects of our lives, from work to leisure activities. Whether it's introducing innovations such as self-driving vehicles and personalized recommendation systems, or improving existing technologies like medical diagnosis and online search algorithms, the the demand for expertise in AI and machine learning is growing rapidly.
This Machine Learning with Python course will give you all the tools you need to get started or advance your knowledge on machine learning with Python.
The course has a strongly practical hands-on focus, and students will gain experience in applying machine learning on real data and interpreting the results. Through lectures and practicals, the students will learn the necessary skills to explore, design, implement, and understand their own machine learning applications. We will cover both fundamental and advanced concepts of machine learning such as data exploration and visualization, supervised and unsupervised models, evaluation, deep learning models and explainability.
Participants should have a basic knowledge of programming and a strong motivation of scripting and programming in Python.
Participants are requested to bring their own laptop computer. Software will be available online
This course can be taken separately, but is also part of a series of 8 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics:
Upon completing, within 5 years, 3 out of 8 courses in the Summer School Data Science specialisation (no more than one text mining course), students can obtain a certificate.
Please see here for more information about the full specialisation.
Academics, researchers, professionals who want to know more and apply some of the most well known models in the field of Machine Learning
The aim of the course is to teach some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, tree-based methods, support vector machines, clustering, deep learning, hidden Markov models, and more.
40 teaching hours
PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.
There are no scholarships available for this course.
The housing costs do not include a Utrecht Summer School sleeping bag. This is a separate product on the invoice. If you wish to bring your own bedding, please deselect or remove the sleeping bag from your order.