Web Analysis for stanford - see.stanford.edu

This course provides a broad introduction to machine learning and statistical pattern recognition. <br><br> 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; VC theory; large margins); reinforcement learning and adaptive control. <br>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.<br> Students are expected to have the following background:<br><br> Prerequisites: - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.<br> - Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)<br> - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.) .During our test, see.stanford.edu was downloaded in 0.0302 seconds. The homepage of the website is of 56.0859 Kb. The homepage was downloaded at the speed of 1.8136 Mbps, which is on the lower side.

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Stanford Engineering Everywhere | CS229 - Machine Learning

Website Inpage Analysis

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HTTP Header Analysis

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