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Bayesian Machine Learning for Optimization in Python

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

4.7

32 Lessons

2 Projects

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LEARNING OBJECTIVES

An understanding of Bayes’ theorem and its applications
Familiarity with the core components of Bayesian machine learning and its application to optimization
Hands-on experience tuning hyperparameters using Bayesian optimization
The ability to attain optimized solutions for complex problems using Bayesian statistics
Familiarity with core components of the Dragonfly framework for scalable Bayesian optimization

Learning Roadmap

32 Lessons1 Project1 Quiz3 Assessments3 Challenges

Introduction to Bayesian Statistics

Learn how to use Bayesian statistics to integrate prior knowledge and handle uncertainty.

An Overview of the Course

An Introduction to Statistics

Frequentist vs. Bayesian Statistics

Maximum Likelihood Estimation and Its Applications

Bayes’ Theorem

Posteriority and Bias in Probability

Applications of Bayes’ Theorem in Software Engineering

Advantages of Bayes’ Theorem over Deterministic Statistics

Bayes’ Theorem in Machine Learning

Bayesian Statistics: Knowledge Check

Assessment

Bayesian Machine Learning

Look at how Bayesian methods enhance machine learning by integrating prior knowledge for robust, probabilistic predictions.

What is Machine Learning?

Incorporating Bayes’ Theorem in Machine Learning

Techniques to Estimate Model Parameters

Gaussian Processes

Regression using Bayes’ Theorem

Understanding the Theory of Bayesian Regression

Application of Bayesian Regression

Regression Using Bayes’ Theorem: Knowledge Check

Assessment

Optimization: An Overview

6 Lessons

Work your way through optimization techniques, including linear, random search, evolutionary, and Bayesian methods, with practical use cases.

Optimization: Knowledge Check

Assessment

Bayesian Optimization: From Scratch

9 Lessons

Grasp the fundamentals of deploying Bayesian optimization for efficient, complex problem-solving and function evaluations.

Hyperparameter Tuning Using Bayesian Optimization

Project

Conclusion

Finish the course with a robust grasp of Bayesian optimization principles, tools, and real-world applications.

Wrap Up

Overcoming Uncertainty with Bayesian Probability in Python

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Danial Amin

I am a doctoral candidate at the University of Vaasa, Finland, under the supervision of Dr. Joni Salminen focusing on Generative AI (GenAI) and user representation research. Currently, I work as an AI Research Scientist at Samsung Design Innovation Center.

Learn more about Danial

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