Bayesian Optimization for Machine Learning Engineers

Course overview

Bayesian Optimization for Machine Learning Engineers 1. Introduction to Bayesian Optimization: Definition and fundamentals of Bayesian Optimization, overview of mathematical concepts, application areas in Machine Learning 2. Mathematical Foundations of Bayesian Optimization: Probability distributions and their application, Gaussian Processes as an optimization tool, explanation of the Acquisition Functions 3. Implementation of Bayesian Optimization: Steps for implementation in Python, use of libraries such as GPyOpt and Scikit-Optimize, tuning parameters for optimal results 4. Evaluation and Performance Measurement: Methods for evaluating optimization processes, KPIs for measuring success, analysis of optimization results 5. Integration into Machine Learning and Data Analysis: Utilizing Bayesian Optimization in Machine Learning pipelines, examples of successful applications, collaboration with other optimization techniques 6. Case Studies: Applications of Bayesian Optimization: Analysis of successful projects and their outcomes, learning points and challenges, discussion of best practices 7. Practical Project: Application of Bayesian Optimization: Development of your own optimization project, documentation of the entire process, presentation of results and feedback 8. Future Trends in Bayesian Optimization: Trends and developments in the field of optimization, expected new techniques and tools, reflection on future challenges

Upcoming dates

• 2026-08-09T22:00:00.000Z · 2027-02-17T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-08-09T22:00:00.000Z · 2027-08-08T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-08-09T22:00:00.000Z · 2027-02-17T23:00:00.000Z · München · Combined Learning
• 2026-08-09T22:00:00.000Z · 2027-08-08T22:00:00.000Z · München · Combined Learning
• 2026-09-17T22:00:00.000Z · 2027-03-28T22:00:00.000Z · Virtuelles Klassenzimmer