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 An outline of this lecture includes: Linear Regression Recap Locally Weighted Regression Probabilistic Interpretation Logistic Regression Newton's method 00:00 Introduction - recap discussion on supervised learning 05:38 Locally weighted regression 05:53 Parametric learning algorithms and non-parametric learning algorithms 21:32 Probabilistic Interpretation 46:18 Logistic Regression 1:05:57 Newton's method #aicourse #andrewng