Bayesian Machine Learning

September 29, 2021 | 8:53 am
Dr Egor Kraev, Head of AI, Wise

In classical machine learning, one looks for the ‘best’ vector of model parameters to fit the data, leading to overfitting if one is not careful, and requiring extra effort to understand model uncertainty. In contrast, in Bayesian machine learning one considers all possible parameter vectors compatible with the data observed so far, giving one a firm handle on model uncertainty and allowing to gently inject prior knowledge. This session will describe the benefits and drawbacks of doing that, and show how to use this approach in practice.


Leave us a message using our contact form and we’ll get back to you straight away.

If you’re eager to get started, give us a call now on 01908 465 570


for reaching out, 🙏

A member of our team will be in touch shortly to arrange our chat.

Mmm 🍪cookies!

We use cookies to make your experience on this website better, and we have a variety to choose from. Use the toggles below to customise your selection or click 'Save my cookies' to get straight to the content.