09-22, 10:15–10:45 (US/Eastern), Main Room
How to Enhance Your Machine Learning Models with Genetic Algorithms
Genetic algorithms are a family of techniques inspired by the principles of evolution and natural selection. They can produce high-quality solutions for problems involving search, optimization and learning, while overcoming some of the hurdles encountered by traditional algorithms. One of the most interesting areas where Genetic Algorithms can be applied is Machine Learning. This hands-on presentation will demonstrate how Genetic Algorithms can be used to tune and optimize the hyperparameters of supervised learning models, as well as for implementing feature selection—improving accuracy and reducing overfitting. The presentation includes code examples written in Python.
Previous knowledge expected
Artificial Intelligence specialist and mentor, a senior software engineer, and a technology community leader. Eyal started his software engineering career as a pioneer in the field of voice over IP, and is currently a member of the data platform team at Gradle, working on the ML-based predictive test selection. Eyal leads the Jacksonville (FL) Java user group as well as the Enterprise AI user group, and writes the developer-oriented blog 'ai4java'. He is the author of the book 'Hands-On Genetic Algorithms with Python' (Packt).