# Machine Learning

## Video Lectures

Displaying all 20 video lectures.

Lecture 1Play Video |
Introduction: Applications of Machine Learning, Logistics of the Class, Learning TheoryLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. |

Lecture 2Play Video |
Linear Regression, Gradient Descent, Normal EquationsLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how relate to machine learning. |

Lecture 3Play Video |
Locally Weighted Regression, Probabilistic Interpretation, Logistic RegressionLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. |

Lecture 4Play Video |
Newton's Method, Exponential Family, Generalized Linear Models (GLMs)Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. |

Lecture 5Play Video |
Learning Algorithms: Generative, Gaussian Discriminant Analysis, DigressionLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. |

Lecture 6Play Video |
Neural networks, Naive Bayes, Support Vector MachinesLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine. |

Lecture 7Play Video |
Optimal Margin Classifier, Primal/Dual Optimization (KKT), SVM Dual, KernelsLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. |

Lecture 8Play Video |
Support Vector Machines: Kernels, Soft Margin, SMO AlgorithmLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. |

Lecture 9Play Video |
Learning Theory I: Bias/Variance, ERM, Union Bound, Hoeffding Inequality, Uniform ConvergenceLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. |

Lecture 10Play Video |
Learning Theory II: Uniform Convergence, VC Dimension, Model SelectionUniform Convergence - The Case of Infinite H, The Concept of 'Shatter' and VC Dimension, SVM Example, Model Selection, Cross Validation, Feature Selection |

Lecture 11Play Video |
Bayesian Statistics and Regularization, Online Learning, Machine Learning AlgorithmsLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. |

Lecture 12Play Video |
Unsupervised Learning, Clustering, Mixtures of Gaussians, Jensen's Inequality, EM AlgorithmLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. |

Lecture 13Play Video |
Expectation Maximization: Mixture of Gaussians, Naive Bayes, Factor AnalysisLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. |

Lecture 14Play Video |
The Factor Analysis EM steps, Principal Component Analysis (PCA)Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). |

Lecture 15Play Video |
PCA: Latent Semantic Indexing, Singular Value Decomposition, Independent Component AnalysisLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. |

Lecture 16Play Video |
Reinforcement Learning I: MDP, Value Function, Value Interation, Policy IterationLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. |

Lecture 17Play Video |
Reinforcement Learning II: Continuous State MDP, Discretization, Models/Simulators, Fitted ValueLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. |

Lecture 18Play Video |
State-action Rewards, Finite Horizon MDPs, Dynamical Systems: Models, LQR, Riccati EquationLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. |

Lecture 19Play Video |
Debugging RL Algorithm, Linear Quadratic Regulation, Kalmer Filters, Linear Quadratic GaussianLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. |

Lecture 20Play Video |
POMDPs, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement LearningLecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. |