Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Curated by: Stanford Online (21 videos)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html 0:00 Introduction 1:15 Unsupervised learning 1:38 First unsupervised learning algorithm 1:54 Market Segmentation 5:33 Clustering algorithm 5:37 K-means clustering 5:52 Initialize the cluster centroids 12:10 Cost function 16:32 Density Estimation 18:01 Anomaly Detection 20:40 Mixture of Gaussians Volatile 29:27 Maximum Likelihood Estimates 31:44 Bayes Rule 48:12 Jensen's Inequality 57:57 Density Estimation Problem 59:32 Maximum Likelihood Estimation 1:07:16 Concave form of Jensen's Inequality