Explore topological data analysis and machine learning in this one-hour lecture by John Harer. Delve into key concepts including methodology, transformer examples, dimension reduction, and sliding windows. Learn about community-accepted features, extractors, binning, and syzygy coordinates. Examine machine learning with persistence through classification from local information and topological trackers. Investigate multi-scale local PCA and shape analysis techniques. Compare feature selections and discover methods for sorting and grabbing data. Gain insights into the intersection of topology and machine learning, enhancing your understanding of advanced data analysis techniques.
John Harer - Topological Data Analysis and Machine Learning
Applied Algebraic Topology Network via YouTube
Overview
Syllabus
Intro
Methodology
Transformer Examples
Dimension Reduction
Sliding Windows (Delay Reconstruction) Jose Perea
Community Accepted Features Chris Traile
Extractors
Binning
Syzygy Coordinates
Machine Learning with Persistence Example 1: Classification from Local Information
Multi-Scale Local PCA
Multi-Scale Local Shape Analysis
Machine Learning with Persistence Example 2: Topological Tracker
Machine Learning on Diagrams
Persistence Features
Comparison of Feature Selections
Sorting and Grabbing
Taught by
Applied Algebraic Topology Network
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