The workshop will take place on August 16, 2023, 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
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).
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.
Workshop Materials
- Session slides
- Colab notebook
- Additional resource: Dot product
- Additional resource: Cross product
- Additional resource: Types of matrices
- Additional resource: Inverse of matrix
- Additional resource: Solving systems of equations
- Additional resource: Eigenvectors
- Additional resource: Eigenvalues
Instructor
Shpresim Sadiku
Shpresim Sadiku is currently a PhD candidate at the Institute of Mathematics, Technische Universität Berlin. He is also affiliated with the Interactive Optimization and Learning (IOL) research lab at the Zuse Institute Berlin, where he holds a Scientific Assistant position. He received an MSc in Mathematics in Data Science at the Technical University of Munich and a BSc in Mathematics at the University of Tirana in July 2017.
Schedule (Central European Summer Time - CEST)
Session Starts
Linear Algebra for Data Science (Part I)
Short Break
Session Continues
Linear Algebra for Data Science (Part II)