The workshop will take place tentatively on July 29, 2022, 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.
How can we run operations and analysis on large quantity of data? We need matrices to represent these data, process the network structure and learning operations to mine for insights. Linear Algebra is an essential branch of mathematics to help make running algorithms on massive datasets feasible. This workshop will help you to develop an understanding in this important branch of mathematics for data science.
This workshop will address students who never attended a Linear Algebra course. The aim is to present the mathematical concepts which are a prerequisites for lectures like Mathematics for Data Science (first), Machine Learning and Causal Inference (later).
Syllabus (key topics)
- Objects: Scalars, vectors, matrices, tensors
- Vector space, linear independence, span, basis, rank
- Operations: transpose, determinant, matrix multiplication, trace, norm, inverse
- If there is still time: Preview to eigenvectors and eigenvalues
- Gilbert Strang - Linear Algebra and Learning from Data - 2019
- Zico Kolter - Linear Algebra Review and Reference - 2008
- Kevin P. Murphy - Probabilistic Machine Learning - 2022
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.
Lewin Stein is currently a Supercomputing & Computational Fluid Dynamics consultant at the Zuse Institute Berlin. He has a Ph.D. in physical engineering from TU Berlin and 10+ years software development with a focus on computational fluid dynamics and acoustics. He is skilled in Fortran, MPI, Python, Matlab and is a interdisciplinary collaborator accustomed to international environments.
Schedule (Central European Summer Time - CEST)
Linear Algebra for Data Science (Part I)
Linear Algebra for Data Science (Part II)