Study Notes in Machine Learning¶
Contents:
- 1. Preliminaries
- 1.1. Overview of Probability Measure Theory
- 1.2. Covariance Matrix
- 1.2.1. Property 1-4. Classic representation of covariance by expectation (or mean).
- 1.2.2. Property 1-5. Invariance to centralization.
- 1.2.3. Theorem 1-5. Matrix arithmetics of covariance matrix.
- 1.2.4. Theorem 1-6. Positive definiteness of covariance matrix.
- 1.2.5. Property 1-6. Sample covariance represneted by rank-1 sum.
- 1.2.6. Theorem 1-7. Block decomposition of covariance matrix.
- 1.3. Multivariate Gaussian Distribution
- 2. Basic Neural Networks