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The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. [3] Founded by researchers from the American Assembly at Columbia University , the OSP has amassed the most extensive collection of searchable syllabi.
The course includes modules on machine learning, neural networks, the philosophy of artificial intelligence, and using artificial intelligence to solve problems. [3] [4] It consists of two parts: Introduction to AI and its sequel, Building AI, that was released in late 2020. [5]
It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.
In 2011, MIT OpenCourseWare introduced the first of fifteen OCW Scholar courses, which are designed specifically for the needs of independent learners. While still publications of course materials like the rest of the site content, these courses are more in-depth and the materials are presented in logical sequences that facilitate self-study.
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Attention in Machine Learning is a technique that mimics cognitive attention. In the context of learning on graphs, the attention coefficient α u v {\displaystyle \alpha _{uv}} measures how important is node u ∈ V {\displaystyle u\in V} to node v ∈ V {\displaystyle v\in V} .
A log-log chart spanning more than one order of magnitude along both axes: Semi-log or log-log (non-linear) charts x position; y position; symbol/glyph; color; connections; Represents data as lines or series of points spanning large ranges on one or both axes; One or both axes are represented using a non-linear logarithmic scale; Streamgraph
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.