Stanford University will offer a free online course in machine learning

Stanford University will offer an online course in Machine Learning with Graphs in the fall of 2022. The training will be free for anyone who wishes to attend. The lectures will take place on Tuesday and Thursday, from 1:30 p.m. to 3:00 p.m. in the NVIDIA auditorium. Stanford faculty will continue to post course slides and assignments online as the course progresses. Full syllabus and course details are available on the official website:

Anyone with a basic understanding of computer science principles sufficient to write a reasonably non-trivial computer program can enroll in the course. According to the eligibility criteria, one must be familiar with the fundamentals of probability theory and have a basic knowledge of linear algebra to understand the online course.

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In the online course, students will be trained to enter complex data that can be represented as a graph of relationships between objects within the course. These networks are essential for the modeling of social, technical and biological systems.

The course focuses on the computational, algorithmic, and modeling challenges associated with massive graph analysis. Students learn machine learning techniques and data mining tools that could provide insights into a variety of networks by studying the graph’s underlying structure and properties.

The course covers the following topics: learning graph representations and neural networks; algorithms for the World Wide Web; reasoning on knowledge graphs; maximizing influence; epidemic detection; and social media analysis.

Stanford University’s machine learning course was the most popular course in 2021, according to Coursera – one of India’s leading ed-tech platforms. In 2020, “Science of Wellbeing by Yale University” was the most chosen course by learners on the platform.

How to Learn Math – a course whose teaching approaches to learning the subject are also among the most favorite online courses offered by Stanford, followed by Artificial Intelligence: Principles and Techniques.

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