1 Mathematics for Machine Learning 📐
Essential mathematical concepts and foundations for understanding machine learning algorithms.
1.1 Topics Covered
1.1.1 Linear Algebra
- Vectors and vector spaces
- Matrices and matrix operations
- Eigenvalues and eigenvectors
- Singular value decomposition (SVD)
1.1.2 Calculus
- Derivatives and gradients
- Partial derivatives
- Chain rule and backpropagation
- Optimization theory
1.1.3 Statistics & Probability
- Probability distributions
- Bayes’ theorem
- Statistical inference
- Hypothesis testing
1.1.4 Optimization
- Gradient descent
- Convex optimization
- Lagrange multipliers
- Constrained optimization
Mathematical rigor meets practical application in machine learning.