Mathematics is one of the prerequisites that most data science enthusiasts fear to get into machine learning. Without learning math, it will be very difficult for you to create your algorithms although it is possible to learn machine learning if you don't have much interest in math. So, if you want to know more about the topics of mathematics you need to learn for machine learning, this article is for you. In this article, I will introduce you to all topics of mathematics for machine learning.
All Topics of Mathematics for Machine Learning
You must have heard that if math is one of your fears, you need to consider whether machine learning is for you or not. If I give you my example, then I am not from an engineering background and I also did not have mathematics in my higher secondary education. I came from a commerce background with an additional subject as Information Practices instead of Mathematics. So how am I doing good in machine learning? The answer is, it depends from person to person. Some people may not understand anything about machine learning if they don't have enough knowledge of mathematics, while others may be so interested in machine learning that they focus more on problem-solving skills which make them experts in machine learning.
A company wants someone who can solve their business problems with machine learning rather than someone who knows the mathematical concepts behind machine learning models. So if you are not very good at math, just go through all the math topics for machine learning so you can answer questions based on the mathematics behind machine learning in your interview. So here are all the topics of mathematics you need to know for machine learning:
- Linear Algebra:
- Linear Equations
- Matrices
- Vector Spaces
- Linear Independence
- Basis and Rank
- Linear Mappings
- Affine Spaces
- Analytic Geometry:
- Norms
- Inner Products
- Lengths and Distances
- Angles and Orthogonality
- Orthonormal Basis
- Orthogonal Complement
- Inner Product of Functions
- Orthogonal Projections
- Rotations
- Matrix Decompositions:
- Determinant and Trace
- Eigenvalues and Eigenvectors
- Cholesky Decomposition
- Eigendecomposition and Diagonalization
- Singular Value Decomposition
- Matrix Approximation
- Matrix Phylogeny
- Vector Calculus:
- Differentiation of Univariate Functions
- Partial Differentiation and Gradients
- Gradients of Matrices
- Useful Identities for Computing Gradients
- Backpropagation and Automatic Differentiation
- Higher-Order Derivatives
- Probability and Distributions:
- Construction of a Probability Space
- Discrete and Continuous Probabilities
- Sum Rule
- Product Rule
- Bayes Theorem
- Summary Statistics and Independence
- Gaussian Distribution
- Linear Regression:
- Problem Formulation
- Parameter Estimation
- Bayesian Linear Regression
- Principal Component Analysis:
- Problem Setting
- Maximum Variance Perspective
- Projection Perspective
- Eigenvector Computation and Low-Rank Approximations
- PCA in High Dimensions
- Density Estimation:
- Gaussian Mixture Model
- Parameter Learning
- EM Algorithm
- Latent-Variable Perspective
Summary
So these were all the topics of mathematics that you need to learn for machine learning. Always remember that a company wants someone who can solve their business problems with machine learning rather than someone who knows the mathematical concepts behind machine learning models. So if you are not that good at mathematics just go through all the topics of mathematics for machine learning so that you can answer questions based on the mathematics behind machine learning. I hope you liked this article on all the topics of mathematics that you should know for machine learning. Feel free to ask your valuable questions in the comments section below.
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