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Introducing the AI, Misinformation, and Policy Seminar Series

NYU Center for Data Science

The AI, Misinformation, and Policy Seminar Series (AMPol) at the Center for Data Science explores this critical research area, featuring speakers working in the intersecting fields of data science, machine learning, and misinformation. To access the lecture slides, please visit Emily Saltz Lecture Slides. by Meryl Phair

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Spatial Intelligence: Why GIS Practitioners Should Embrace Machine Learning- How to Get Started.

Towards AI

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? You just want to create and analyze simple maps not to learn algebra all over again.

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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

Data scientists use algorithms for creating data models. Basics of Machine Learning. Machine learning is the science of building models automatically. Whereas in machine learning, the algorithm understands the data and creates the logic. In supervised learning, a variable is predicted.

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Blending Theory and Utility: The Vision and Impact of CDS’s MaD Group

NYU Center for Data Science

The group, however, quickly became well-known for a seminar that still serves as its flagship: the MaD seminar. Bruna and the early organizers of the MaD group crafted this seminar to be a nexus of research on the theoretical foundations of data science and machine learning.

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How to Choose the Best Data Science Program

Pickl AI

Continuous Learning and Growth The field of Data Science is constantly evolving with new tools and technologies. Enrolling in a Data Science course keeps you updated on the latest advancements, such as machine learning algorithms and data visualisation techniques.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

Moving across the typical machine learning lifecycle can be a nightmare. Machine learning platforms are increasingly looking to be the “fix” to successfully consolidate all the components of MLOps from development to production. What is a machine learning platform? That’s where this guide comes in!

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When Should AI Step Aside? Understanding Cultural Values in AI Systems

NYU Center for Data Science

Several months later, he returned to Africa to lead a responsible AI mentorship session at the 2024 Deep Learning Indaba in Dakar, Senegal. This fieldwork informed Bhatt’s research on “algorithmic resignation” — the strategic withdrawal of AI systems in scenarios where human judgment better serves community values.

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