Machine Learning

Video Lectures

Displaying all 20 video lectures.
Lecture 1
Introduction: Applications of Machine Learning, Logistics of the Class, Learning Theory
Play Video		 Introduction: Applications of Machine Learning, Logistics of the Class, Learning Theory
Lecture 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 2
Linear Regression, Gradient Descent, Normal Equations
Play Video		 Linear Regression, Gradient Descent, Normal Equations
Lecture 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 3
Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression
Play Video		 Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression
Lecture 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 4
Newton's Method, Exponential Family, Generalized Linear Models (GLMs)
Play 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 5
Learning Algorithms: Generative, Gaussian Discriminant Analysis, Digression
Play Video		 Learning Algorithms: Generative, Gaussian Discriminant Analysis, Digression
Lecture 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 6
Neural networks, Naive Bayes, Support Vector Machines
Play Video		 Neural networks, Naive Bayes, Support Vector Machines
Lecture 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 7
Optimal Margin Classifier, Primal/Dual Optimization (KKT), SVM Dual, Kernels
Play Video		 Optimal Margin Classifier, Primal/Dual Optimization (KKT), SVM Dual, Kernels
Lecture 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 8
Support Vector Machines: Kernels, Soft Margin, SMO Algorithm
Play Video		 Support Vector Machines: Kernels, Soft Margin, SMO Algorithm
Lecture 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 9
Learning Theory I: Bias/Variance, ERM, Union Bound, Hoeffding Inequality, Uniform Convergence
Play Video		 Learning Theory I: Bias/Variance, ERM, Union Bound, Hoeffding Inequality, Uniform Convergence
Lecture 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 10
Learning Theory II: Uniform Convergence, VC Dimension, Model Selection
Play Video		 Learning Theory II: Uniform Convergence, VC Dimension, Model Selection
Uniform Convergence - The Case of Infinite H, The Concept of 'Shatter' and VC Dimension, SVM Example, Model Selection, Cross Validation, Feature Selection
Lecture 11
Bayesian Statistics and Regularization, Online Learning, Machine Learning Algorithms
Play Video		 Bayesian Statistics and Regularization, Online Learning, Machine Learning Algorithms
Lecture 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 12
Unsupervised Learning, Clustering, Mixtures of Gaussians, Jensen's Inequality, EM Algorithm
Play Video		 Unsupervised Learning, Clustering, Mixtures of Gaussians, Jensen's Inequality, EM Algorithm
Lecture 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 13
Expectation Maximization: Mixture of Gaussians, Naive Bayes, Factor Analysis
Play Video		 Expectation Maximization: Mixture of Gaussians, Naive Bayes, Factor Analysis
Lecture 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 14
The Factor Analysis EM steps, Principal Component Analysis (PCA)
Play 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 15
PCA: Latent Semantic Indexing, Singular Value Decomposition, Independent Component Analysis
Play Video		 PCA: Latent Semantic Indexing, Singular Value Decomposition, Independent Component Analysis
Lecture 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 16
Reinforcement Learning I: MDP, Value Function, Value Interation, Policy Iteration
Play Video		 Reinforcement Learning I: MDP, Value Function, Value Interation, Policy Iteration
Lecture 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 17
Reinforcement Learning II: Continuous State MDP, Discretization, Models/Simulators, Fitted Value
Play Video		 Reinforcement Learning II: Continuous State MDP, Discretization, Models/Simulators, Fitted Value
Lecture 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 18
State-action Rewards, Finite Horizon MDPs, Dynamical Systems: Models, LQR, Riccati Equation
Play Video		 State-action Rewards, Finite Horizon MDPs, Dynamical Systems: Models, LQR, Riccati Equation
Lecture 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 19
Debugging RL Algorithm, Linear Quadratic Regulation, Kalmer Filters, Linear Quadratic Gaussian
Play Video		 Debugging RL Algorithm, Linear Quadratic Regulation, Kalmer Filters, Linear Quadratic Gaussian
Lecture 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 20
POMDPs, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning
Play Video		 POMDPs, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning
Lecture 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.