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Machine Learning

Machine learning in a Nutshell:

• Data: Raw Environment
• Model: Maps input --> Output
• Learning: Optimizes Models by finding better parameters to models (patterns).
• Predictor: A MLA that is a system that makes predictions
• We Represent Data as Vectors
• Choose a Model
• Optimize using numerical methods

The mathematics needed:

• Linear Algebra
• Systems of Equations
• Matrices
• Vector Spaces
• Linear Mappings
• Affine Spaces
• Analytic Geometry
• Matrix Decomposition
• Vector Calculus
• Probability Distributions
• Continuous Optimisation
• Models vs Data
• Linear Regression
• Dimensionality
• Principal Component Analysis
• Density Estimation (Gaussian Mixture Models
• Classification with Support Vector Machines

Sources, Links and Reading Lists

• The 100pg ML Book
• Python --- SciPy --- NumPy
• building-machine-learning-systems-in-python
• Metaoptimize
• Stack Exchange
• Two To Real
• PacktPub
• (Numerical Linear Algebra for Programmers | New Book Chapter Available)
• How Neural Networks Work: 3h50min
• Machine Learning Fundamentals: Bias and Variance
• machine learning and javascript - w3schools
• machinelearningjs

http://thevikidtruth.com/wiki/?machinelearning

03aug22

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