- Lecture 1. Review of Linear Algebra
- Lecture 2. Review of Probability
- Lecture 3. Review of Optimization
- Lecture 4. PCA. Intro to Bayesian Decision Theory
- Lecture 5. Expected Risk Minimization (ERM). Max Likelihood (ML), MAP Decision rule.
- Lecture 6. ERM. ML, MAP, P(error). ROC.
- Lecture 7. MAP with Gaussian class conditionals
- Lecture 8. Parametric estimation. ML, MAP with Poisson, Gamma
- Lecture 9. Cramer Rao lower bound. Gaussian Bayesian Estimators. Prior as regularizer.
- Lecture 10. k means. Expectation Maximization. GMM. Factor analysis
Re: ./gen.py. Usage- Generates image numbers from untarred scanned image DIR into markdown format.
python gen.py <DIR name>
Example-
python gen.py L3