Pattern Recognition and Machine Learning

Code is extremely compact, minimizing length to make core algorithms easily identifiable, as emphasized in the Design Goal for succinctness.

Uses vectorization and matrix factorization, making functions orders of magnitude faster than Matlab built-ins like kmeans, as noted for efficiency.

Implements techniques like computing probabilities in logarithm domain and square root matrix updates to ensure robustness, preventing numerical issues in critical calculations.

Heavily commented with PRML formulas and symbols synchronized with the book, enhancing readability and making it ideal for learners following the textbook.