Machine Learning

Mathematics for ML:

1. Statistics & Probability
Populations & Sampling
Mean, Median, Mode & Expected Values
Variance & Covariance
Random Variables
Common Probability Distributions (normal, binomial & uniform)
Central Limit Theorem
Conditional Probability
Bayes’ Theorem
Maximum Likelihood Estimation (MLE)
Linear & Logistic Regression

2. Linear Algebra
Scalars, Vectors, Matrices & Tensors
Matrix Operations (Addition, Subtraction, Multiplication, Transpose, Determinant & Inverse)
Matrix Rank & Linear Independence
Eigenvalues & Eigenvectors
Matrix Decompositions (e.g., SVD)
Principal Component Analysis (PCA)

3. Calculus
Derivatives & Gradients
Gradient Descent Algorithm
Vector/Matrix Calculus (Jacobian, Hessian)
Chain Rule
Fundamentals of Optimisation (Local vs. Global Minima, Saddle Points & Convexity)