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Bayesian Machine Learning - Benefits, Drawbacks, and Practical Applications

Offered By: Data Science Festival via YouTube

Tags

Data Science Courses Machine Learning Courses Statistical Inference Courses Probability Theory Courses Parameter Estimation Courses Overfitting Courses

Course Description

Overview

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Explore the world of Bayesian machine learning in this 35-minute conference talk from the Data Science Festival Summer School. Delve into the advantages of considering all possible parameter vectors compatible with observed data, providing a robust approach to model uncertainty and allowing for the gentle injection of prior knowledge. Compare this method to classical machine learning techniques, which focus on finding the 'best' vector of model parameters to fit data, potentially leading to overfitting issues. Learn about the benefits and drawbacks of the Bayesian approach, and discover practical applications from Egor Kraev, Head of AI at Wise. Gain insights into handling model uncertainty, avoiding overfitting, and leveraging prior knowledge in your machine learning projects.

Syllabus

Bayesian Machine Learning


Taught by

Data Science Festival

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