Stanford University Network chart new courses CS224W-2019- machine learning algorithms - Video sharing resources and ppt

Course description

Network modeling complex basic tool for social, technological and biological systems. Combined with the availability of online social networks and the emergence of large-scale data in biological sciences, this course focuses on large-scale networks, these large-scale network computing presents some challenges, algorithms and modeling. By learning their underlying network structure and connection relationship, to introduce students to the machine learning and data mining tools and data mining tools easy to reveal insights into society, technology and the natural world.
Topics include: the food chain and financial markets robustness and fragility; the World Wide Web algorithm; and Figure neural network learns; identifying biological network function module; detecting disease outbreaks.

End of the text attached to this course ppt and the latest video tutorial download address .

Course Home

http://web.stanford.edu/class/cs224w/index.html#content

Course presenter and assistant

This course is taught by himself GraphSage author Jurij Leskovec.

Course Topics

basic condition

Students should have the following background:

Have a basic knowledge of principles of computer science, rational enough to write computer programs (for example, recommended CS107 or CS145 or equivalent background)

Familiar with the basic probability theory (CS109 or Stat116 enough but not required)

Familiar with the basic linear algebra (Math 51, Math 103, Math 113, or in any one of CS 205 are far beyond the necessary)

Course Outline