Lectures
- 1. Overview
- 2. Linear Regression, Gradient Descent, Normal Equations
- 3. Locally Weighted Regression, Linear Regression, Logistic Regression
- 4. Newton's Method, Exponential Family, General Linear Models
- 5. Generative Algorithms, Gaussian Discriminant Analysis
- 6. Applications of Neural Network, Support Vector Machine
- 7. Optimal Margin Classifier, Karush-Kuhn-Tucker (KKT) Conditions, SVM Dual
- 8. Non-linear Decision Boundaries and Soft Margin SVM
- 9. Bias-variance Tradeoff, Empirical Risk Minimization, The Union Bound, Hoeffding Inequality
- 10. Uniform Convergence, VC Dimension, Model Selection
- 11. Bayesian Statistics and Regularization, Applying Machine Learning Algorithms
- 12. Unsupervised Learning, Mixtures of Gaussians and the EM Algorithm, Jensen's Inequality
- 13. Mixture of Gaussian, Mixture of Naive Bayes, Factor Analysis Model
- 14. Factor Analysis Model, EM for Factor Analysis, Principal Component Analysis
- 15. Independent Component Analysis (ICA)
- 16. Reinforcement Learning, Markov Decision Process, Value Function
- 17. Continuous States, Discretization & Curse of Dimensionality
- 18. State-action Rewards, Finite Horizon MDPs, Dynamical Systems
- 19. Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR),
- 20. Partially Observable MDPs, Reinforce Algorithm, Pegasus Algorithm
Machine Learning - Lecture 2
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Lecture 2 - Linear Regression, Gradient Descent, Normal Equations
An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations
Prof. Andrew Ng
CS229 Machine Learning (Stanford University: Stanford Engineering Everywhere) http://see.stanford.edu Date accessed: 2009-05-07 License: Creative Commons Attribution 3.0 |
