AI Exam - Math: 1B - Vectors

 

AI Practitioner Exam Prep - Vectors

Abbreviations
PCA = Principal Component Analysis algorithm

Math Terms
Dimension = number of coordinates to plot point in vector math. Almost same as feature. 
Feature = attribute of piece of the data. ex = "age". Almost same as dimension.
Vectors = numerical N-dimensional arrays. Ex: X, Y, and Z coordinates if 3-D array.
Vector Space = cloud shape of all the points of all of the vectors.

Dimensionality Reduction Terms
  Linear D. R. Terms (so only lines or planes)
  Linear Discriminant Analysis - SL. think event planner (so SL) doing seating arrangements grouping max space BETWEEN groups, while min distance WITHIN a group). Reduces dimensions to reduce costs. Goal: improve class predictability.
  PCA = UL. think: looks for Patterns, Compressing it (reducing the dimensions), on the Anonymous data (so UL).  PC1 = trend of points, PC2 = perpendicular and sub-trend. Ex: Does not care about labels (of "height" and "weight"), but rather creates single dimension of size (so seeing the trend) which is PC1. Then tracks data that is not explainable by size (say "body shape") that is PC2.  Only cares about where the data is most spread out (variance).


  Principal Components = new, independent axes (directions) that rank the data's most important trends (patterns) from highest to lowest spread (variance)."

  Non-Linear/Manifold D. R. Terms
  Autoencoders = UL. has encoder and decoder. works by bottlenecking. ex: detects anomalies in sensor data. 

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