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Abstract
Introductory course to Mathematical aspects of Signal Processing, Network Theory, and Machine Learning. It showcases how different areas of Mathematics (including, but not limited to: Linear Algebra, Probability, Number Theory, Statistics, Combinatorics) interact and find applications in Data Science and related fields.
Objective
Introduction to Mathematical aspects of Signal Processing, Network Theory, and Machine Learning. This course also aims to showcase how different areas of Mathematics (including, but not limited to: Linear Algebra, Probability, Number Theory, Statistics, Combinatorics) interact and find applications in Data Science and related fields.
Content
Mathematical aspects of Supervised Learning, Unsupervised Learning, Sparsity, and Networks. This course is a Mathematical course, with Theorems and Proofs.
Resources
Lecture Notes
https://people.math.ethz.ch/~abandeira//MathofSNLnotes2025.pdf