Learn Bayesian optimization and statistical modeling to tackle high-dimensional problems. Explore hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

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Bayesian optimization allows developers to leverage Bayesian inference and statistical modeling to efficiently search for the optimal solution in a high-dimensional space. 

Starting with the fundamentals of statistics and Bayesian statistics, you’ll explore different concepts of machine learning and its applications in software engineering. Next, you’ll discover different strategies for optimizations. Through practical examples and hands-on exercises, you’ll gain proficiency in implementing Bayesian optimization algorithms and fine-tuning them for specific tasks. By the end of the course, you’ll have a comprehensive understanding of the entire Bayesian optimization workflow, from problem formulation to solution optimization.

By completing this course, you’ll be able to tackle complex optimization problems more efficiently and effectively. You’ll be equipped to find optimal solutions in areas such as hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

Bayesian Machine Learning for Optimization in Python

Learn about the different Bayesian optimization libraries and frameworks that exist in Python.

Introduction to Bayesian Statistics

Bayesian Statistics: Knowledge Check

Bayesian Machine Learning

Regression Using Bayes’ Theorem: Knowledge Check

Optimization: An Overview

Optimization: Knowledge Check

Bayesian Optimization: From Scratch

Hyperparameter Tuning Using Bayesian Optimization

Conclusion

Overcoming Uncertainty with Bayesian Probability in Python

Important Libraries and Frameworks

Libraries in Python

Common libraries for Bayesian optimization