The workshop will take place on August 6, 2025, on Zoom and Youtube Livestreaming
About The Data Science Summer School
The Data Science Summer School is a series of theoretical and practical workshops on the exciting methods and technologies currently employed by industry, government, and civil society to address the world's most complex problems today. It is organized by the Hertie School Data Science Lab with funding and support from the Hertie School and the Dieter Schwarz Foundation
Workshop Details
Many data science challenges, particularly those in machine learning and deep learning, are essentially optimisation problems. Ever wondered how exactly a logistic regression algorithm is implemented? Or how to use gradient descent? To understand how these methods work, you need to use concepts from Calculus - gradient, derivatives, limits, and chain rule. This workshop will help you to develop an understanding in this important branch of mathematics for data science.
Content Licensing
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
Instructor
Ilja Klebanov
Ilja Klebanov is a mathematician working in the research group "Numerical Mathematics of Deterministic and Stochastic Partial Differential Equations" of Claudia Schillings at the Free University of Berlin. His research focuses on uncertainty quantification as well as probabilistic approaches to numerical analysis and machine learning. In particular, he is working on high-dimensional approximation of probability distributions, Bayesian inverse problems, density estimation and kernel methods.
Workshop Materials
To be updated
Schedule (Central European Summer Time - CEST)
Session Starts
Calculus for Data Science (Part I)
Short Break
Session Continues
Calculus for Data Science (Part II)