Andrew-Ng-Machine-Learning-Notes

The notes of Andrew Ng Machine Learning in Stanford University

• 1. Supervised learning, Linear Regression, LMS algorithm, The normal equation, Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression

• 2. Generative Learning algorithms, Gaussian discriminant analysis, Naive Bayes, Laplace smoothing, Multinomial event model

• 3. SVM, Lagrange duality, Kernel, SMO

• 4. Bias-Variance trade-off, Learning Theory

• 5. Cross-validation, Feature Selection, Bayesian statistics and regularization

• 6. Online Learning, Online Learning with Perceptron

• 7a. K-Means

• 7b. EM Algorithm, GMM

• 8. EM Algorithm in detail

• 9. Factor Analysis, EM for Factor Analysis

• 10. PCA

• 11. Independent Component Analysis(ICA)

• 12. Reinforcement Learning

• Tree based ML algorithms

• Deep Learning Notes