As I am trying to learn about machine learning, and from my experience learning is done best by explaining, I decided to write every once in a while notes about what I learned.
These are notes coming from mathematical perspective, where I emphasize my intuition though I will try to keep it as basic as possible and self contained. Of course, any feedback would be appreciated, in particular from machine learning experts.
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- Borsuk-Ulam
- Cayley-Hamilton Theorem
- characters
- Chinese remainder theorem
- Compression
- Diophantine approximation
- Dirichlet's theorem
- Dirichlet's approximation theorem
- Dirichlet's unit theorem
- Domino
- Dynamics
- eigenvalues
- Entropy
- Euclid
- Fourier transform
- gaussian integers
- Generic Matrix
- Geometry
- Geometry of numbers
- Ham sandwich
- Homogeneous spaces
- Hyperbolic space
- intermediate value theorem
- isoperimetric inequality
- Klein group
- lattice
- Lattices
- Linearity testing
- mediant
- Minkowski's Theorem
- mobius
- Number Theory
- Peg Solitaire
- Pell's equations
- planar geometry
- poinare disk
- prime numbers
- Pythagoras
- radar
- random walk
- self similar sets
- shortest vector problem
- Sierpinski carpet
- Sierpinski triangle
- SL_2(Z)
- stochastic matrices
- Weak Law of Large Numbers
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