math and stats
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SPSS
Lecture #1: Introduction to linear and non-linear dimensionality reduction. Brief overview of spectral and graph-based methods, such as principal component analysis (PCA), multi dimensional scaling (MDS), ISOMAP, LLE, Laplacian embedding, etc. Further readings: L.
Manifold Learning
Stats V1
"As a professional medical statistician of some 40 years standing, I can unreservedly recommend this textbook as a resource for self-education, teaching and on-the-fly illustration of specific statistical methodology in one-to-one statistical consulting. The topic area coverage is unparalleled in a single textbook, drawing in the usual suspects, but many not-so-usual: Classification trees, Neural networks, Structural equation modeling, to name but a few. This book is an absolute friend-in-need when you need to get to the point fast, without fuss, in a way that anyone can understand."
Credit Scoring, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook