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 1
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Lecture 1 - Overview
The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning
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 |
