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Notes for Foundations in Data Analysis course @ TUM, summer 2022. Obsidian Markdown.

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images
images
 
 
.gitignore
.gitignore
 
 
1 Basics.md
1 Basics.md
 
 
2.1 SVD - Principal Components.md
2.1 SVD - Principal Components.md
 
 
2.2 SVD - The Leading Singular Vector.md
2.2 SVD - The Leading Singular Vector.md
 
 
2.3 SVD - Further Singular Vectors.md
2.3 SVD - Further Singular Vectors.md
 
 
2.4 SVD - Best Rank-k Approximation.md
2.4 SVD - Best Rank-k Approximation.md
 
 
2.5 SVD - The Power Method.md
2.5 SVD - The Power Method.md
 
 
2.6 SVD - Applications, Under- and Overdetermined Systems.md
2.6 SVD - Applications, Under- and Overdetermined Systems.md
 
 
2.7 SVD - Stability of the SVD.md
2.7 SVD - Stability of the SVD.md
 
 
3.1 Probability Basics - Essentials.md
3.1 Probability Basics - Essentials.md
 
 
3.2 TODO.md
3.2 TODO.md
 
 
3.3 TODO.md
3.3 TODO.md
 
 
4 Dimensionality Reduction - Johnson-Lindenstrauss Lemma.md
4 Dimensionality Reduction - Johnson-Lindenstrauss Lemma.md
 
 
5.1 Convexity - Convex Sets.md
5.1 Convexity - Convex Sets.md
 
 
5.2 Convexity - Convex Functions.md
5.2 Convexity - Convex Functions.md
 
 
5.3 Convexity - Convex Conjugate.md
5.3 Convexity - Convex Conjugate.md
 
 
5.4 Convexity - Subdifferential.md
5.4 Convexity - Subdifferential.md
 
 
5.5 Convexity - Convex Optimization.md
5.5 Convexity - Convex Optimization.md
 
 
6.1 TODO - Numerical Optimization - Gradient Descent.md
6.1 TODO - Numerical Optimization - Gradient Descent.md
 
 
6.2 Numerical Optimization - Stochastic Gradient Descent.md
6.2 Numerical Optimization - Stochastic Gradient Descent.md
 
 
6.3, 6.4 - A Primal Dual Algorithm; Nonconvex Optimization with Convex Approaches.md
6.3, 6.4 - A Primal Dual Algorithm; Nonconvex Optimization with Convex Approaches.md
 
 
README.md
README.md
 
 

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About these notes

This repository contains my notes on the Foundations in Data Analysis course held in the summer term 2022 by Prof. Felix Krahmer and Dr. Anna Veselovska at TUM. The notes are based on the course slides 1. Images are taken from the course slides.

The notes are written in Obsidian markdown and are best viewed in Obsidian.

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  1. Felix Krahmer - Foundations in Data Analysis, lecture slides ↩

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Notes for Foundations in Data Analysis course @ TUM, summer 2022. Obsidian Markdown.

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