Manifold Learning: Introduction and Foundational Algorithms

Introduction:
- Overview of manifolds and the basic topology of data
- Statistical learning and instrinsic dimensionality
- The manifold hypothesis
Chapter 1: Multidimensional Scaling
- Classical, metric, and non-metric MDS algorithms
- Example applications to quantitative psychology and social science
Chapter 2: ISOMAP
- Geodesic distances and the isometric mapping algorithm
- Implementation details and applications with facial images and coil-100 object images
Chapter 3: Local Linear Embedding
- Locally linear reconstructions and optimization problems
- Example applications with image data
Chapter 4: Laplacian Eigenmaps/Spectral Embedding
- From the general to the discrete Laplacian operators
- Visualizing spectral embedding with the networkx library
- Spectral embedding with NLTK and the Brown text corpus

Name	Name	Last commit message	Last commit date
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images	images
Isomap.ipynb	Isomap.ipynb
Laplacian-Eigenmaps.ipynb	Laplacian-Eigenmaps.ipynb
Locally_Linear_Embedding.ipynb	Locally_Linear_Embedding.ipynb
Manifold_Learning_Intro.ipynb	Manifold_Learning_Intro.ipynb
Multidimensional_Scaling.ipynb	Multidimensional_Scaling.ipynb
README.md	README.md
_config.yml	_config.yml
custom_style.css	custom_style.css

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