Mathematics for Machine Learning

1. Statistics

• Descriptive Statistics

  • Mean

  • Median

  • Mode

• Inferential Statistics

• Measures of Relationship

  • Correlation

  • Covariance

• Probability Distributions

  • Normal Distribution

  • Binomial Distribution

  • Poisson Distribution

• Applications of Statistics in Machine Learning

2. Probability

• Basics of Probability

• Types of Probability

  • Classical Probability

  • Empirical Probability

  • Subjective Probability

• Conditional Probability

• Bayes’ Theorem

• Random Variables

• Probability Distributions

• Applications of Probability in Machine Learning

3. Linear Algebra

• Scalars

• Vectors

  • Why Vectors are Used in Machine Learning

  • Vector Operations

  • Vector Transformation

• Matrices

  • Matrix Operations

  • Matrix Transpose

  • Determinant

  • Inverse of a Matrix

• Eigenvalues and Eigenvectors

• Applications of Linear Algebra in Machine Learning

4. Calculus

• Limits

• Derivatives

• Partial Derivatives

• Gradients

• Gradient Descent

• Cost Function Optimization

• Applications of Calculus in Machine Learning

5. Optimization Techniques

• Gradient Descent Variants

  • Batch Gradient Descent

  • Stochastic Gradient Descent

  • Mini-Batch Gradient Descent

• Learning Rate

• Loss Function Design

6. Dimensionality Reduction

• Principal Component Analysis (PCA)

• Singular Value Decomposition (SVD)

• Applications of Dimensionality Reduction in Machine Learning

7. Sequences and Series

• Sequences

• Series

• Convergence and Divergence