On July 23, Andrii Latysh will hold the webinar on the modern direction of machine learning — interpretable machine learning. During the webinar, Andrii will tell not only about the motivation and advantages of this approach but also about specific modern methods and ways of constructing interpretable machine learning.
10% of the money for the purchased tickets will be donated for the Charity campaign of Group of Active Rehabilitation.
Report language: Ukrainian
Founder and Coordinator at Odyssey DS community;
ML/DS Engineer at The Product Engine;
Lecturer at Mechnikov University and Hillel;
Andrii Latysh works as a data science and machine learning engineer at The Product Engine. If necessary, he consults teams inside and outside the company. Andrii is studying at the graduate school of ONU Mechnikov, where he also teaches. He also teaches machine learning at Hillel Computer School. Andrii is the founder and coordinator of the Odessa Data Science Community — “Odyssey”, that is devoted to creating opportunities for the professional growth of participants and promoting the development of DS direction in Odessa.
Today, machine learning continues to develop actively and spread in countless and diverse areas. However, the direction of interpretable machine learning is gaining popularity. This direction focuses on building systems and solutions that are clear and accessible to humans. Interpretable machine learning provides a number of important benefits, including the ability to explain to the user in areas where it is important or required by law, greater stability and reliability of the solution, achieving higher quality models through a better understanding of its design and a number of general and situational advantages.
During our conversation, we will talk not only about the motivation and advantages of this approach but also about specific modern methods and ways of constructing interpretable machine learning. Therefore, the webinar should be interested in both enthusiasts and more experienced machine learning engineers who want to learn more about building human-readable solutions.