Mathematics

These are my personal study notes taken while working through the Mathematics for Machine Learning and Data Science Specialization offered by DeepLearning.AI on Coursera. All theoretical content, definitions, and examples are derived from that course. My own contributions are limited to additional annotations, connections to telecommunications and network engineering, and Python implementations.

Source: Coursera. Mathematics for Machine Learning and Data Science Specialization. DeepLearning.AI. Retrieved from https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science

Areas of Study

Linear Algebra

Linear algebra is about manipulating vectors and matrices to do powerful calculations. You will learn these topics:

  • Systems of Equations
  • Solving Systems of Linear Equations: Elimination
  • Solving Systems of Linear Equations: Row Echelon Form and Rank
  • Vector Algebra
  • Linear Transformations
  • Single Perceptron Neural Networks for Linear Regression
  • Determinants In-depth
  • Eigenvalues and Eigenvectors

Calculus

Differential and integral calculus, optimization methods.

Probability & Statistics

Probability theory, statistical inference, and stochastic processes.

Notes for graduate research and professional development.