Math for machine learning
Why Math?
As we learn in school, mathematic is one of the scariest subject in science world, it is full of numbers, formulas and abstract concepts that seems very difficult to understand. But don’t worry because I won’t discuss about the formulas, calculus or Pi. Here I just want to share what topics in math you need to understand the magic behind Machine Learning.
Here are the mathemical foundations for ML:
- Linear Algebra
- Analytic Geometry
- Matric Decompositions
- Vector Calculus
- Probability and Distributions
- Continues Optimization
I know the list is sucks, I mean it sounds sucks but it is pretty fun actually. Really? yeah let me tell you everything about those six concepts.
1. Linear Algebra
What is this all about? Well LA is the study of vectors and certain rules to manipulate vectors (Deisenroth et al., 2020). Sounds easy right? Okay here we go. In general, vectors are special objects that can be added together and multiplied by scalars to produce another object of the same kind.