Embarking on the journey of Machine Learning? A solid grounding in mathematical principles is crucial. Discover the six foundational math concepts that will equip you with the necessary tools to delve into the fascinating world of Machine Learning.
Why learn math for machine learning?
Math helps you select the correct machine learning algorithm
- Understanding math gives you insight into how the model works
- Estimating how confident we are with the model result by producing the right confidence interval and uncertainty measurements needs an understanding of math
- You could develop a customized model that fits your own problem by knowing the machine learning model’s math
Optimization
In the learning objective, training a machine learning model is all about finding a good set of parameters.
- This is what optimization algorithms are for
- Commonly, objective functions in machine learning are trying to minimize the function
- The point at which a function best values takes the minimum value is called the global minima
- Unconstrained Optimization is an optimization function where we find a minimum of a function under the assumption that the parameters can take any possible value (no parameter limitation), while constrained optimization simply limits the possible value by introducing a set of constraints
Machine Learning Math
The math subject is:
- Six math subjects become the foundation for machine learning
- Each subject is intertwined to develop our machine learning model and reach the “best” model for generalizing the dataset
- There are math foundations that are important for Machine Learning.
Vector Calculus
The derivative is a function of real numbers that measure the change of the function value (output value) concerning a change in its argument (input value)
- Differentiation is the action of computing a derivative
- Partial Derivative: a derivative function where several variables are calculated within the derivative function with respect to one of those variables, and the other variables are held constant
- Gradient: a word related to the derivative or the rate of change of a function; you might consider it a fancy word for derivative
Linear Algebra
This is a branch of mathematics concerned with the study of vectors and certain rules to manipulate the vector.
- In machine learning, it is defined as the part of mathematics that uses vector space and matrices to represent linear equations.
- Vectors are special objects that can be added together and multiplied by scalars to produce another object of the same kind.
Probability and Distribution
Probability is a study of uncertainty
- A time where an event occurs or the degree of belief about an event’s occurrence
- The probability distribution is a function that measures the probability of a particular outcome (or probability set of outcomes) that would occur associated with the random variable
- In math, we define probability as a model of some process where random variables capture the underlying uncertainty, and we use the rules of probability to summarize what happens
- in statistics, we try to figure out the underlying process of something that has happened and try to explain the observations
- Machine learning is close to probability because its goal is to construct a model that adequately represents the process that generated the data
Conclusion Machine Learning is an everyday tool that Data scientists use to obtain the valuable pattern we need. Learning the math behind machine learning could provide you an edge in your work.
There are 6 subjects that matter the most when we are starting learning machine learning math
- Linear Algebra
- Analytic Geometry
- Matrix Decomposition
- Vector Calculus
- Probability and Distribution
- Optimization
Analytic Geometry (Coordinate Geometry)
This study is concerned with defining and representing geometrical shapes numerically and extracting numerical information from the shapes numerical definitions and representations
- Analytic geometry is a study in which we learn the data (point) position using an ordered pair of coordinates
- Distance Function
- Provides numerical information for the distance between the elements of a set
- Euclidean Distance Equation
- Inner Product
- Introduces intuitive concepts such as the length of a vector and the angle between two vectors
Matrix Decomposition
This is a study that concerning the way to reducing a matrix into its constituent parts
