Welcome to Mathematics for Machine learning

Quick definitions and intuitive explanations around machine learning and Deep learning.

Contents

• Mathematical Notation

Probability

• What is Probability
• Bayes Theorem
• Random Variables
• Discrete Distributions
• Continuous Distributions
• Joint Distributions
• Covariance and Correlation
• Estimators and Sampling Distributions
• Moments Generating Functions
• Maximum Likelihood Estimation
• Confidence Interval
• Hypothesis Testing

Deep learning

• What is Deep Learning
• Vectors, Matrices, and Tensors
• Loss Functions
• Evaluation Metrics

Mathematics

• Calculus
  • Derivatives
• Linear Algebra
  • Multiplying Matrices and Vectors
  • Hadamard product & element-wise product
  • Dot product
  • Identity and Inverse Matrices
  • Norm
  • The Trace Operator
  • Transpose
  • Diagonal matrix
  • Symmetric matrix
  • Unit Vector
  • Orthogonal Matrix or Orthonormal Vectors
  • Eigendecomposition
  • Singular Value Decomposition
  • The Moore-Penrose Pseudoinverse
  • Principal Components Analysis (PCA)
• Statistics
  • Mean, Variance and Standard Deviation
  • Covariance and Correlation

Practice

• Probability solutions
• R Solutions

Indices and tables

• Index

• Module Index

• Search Page