Sessions

Lecture 1 | Machine Learning (Stanford)

Lecture 1 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent […]
Lecture 6 | Machine Learning (Stanford)

Lecture 6 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]
Lecture 9 | Machine Learning (Stanford)

Lecture 9 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]
Lecture 17 | Machine Learning (Stanford)

Lecture 17 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]
Lecture 16 | Machine Learning (Stanford)

Lecture 16 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
Lecture 3 | Machine Learning (Stanford)

Lecture 3 | Machine Learning (Stanford)

9 years ago
Help us caption and translate this video on Amara.org: www.amara.org/en/v/BGwS/ 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. This course provides a broad introduction to machine learning and […]
Lecture 7 | Machine Learning (Stanford)

Lecture 7 | Machine Learning (Stanford)

9 years ago
Help us caption and translate this video on Amara.org: www.amara.org/en/v/zJX/ 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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]
Lecture 8 | Machine Learning (Stanford)

Lecture 8 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and […]
Lecture 15 | Machine Learning (Stanford)

Lecture 15 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
Lecture 19 | Machine Learning (Stanford)

Lecture 19 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]
Lecture 10 | Machine Learning (Stanford)

Lecture 10 | Machine Learning (Stanford)

9 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and […]
Lecture 11 | Machine Learning (Stanford)

Lecture 11 | Machine Learning (Stanford)

9 years ago
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. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning […]