NYU Center for Data Science
MARCH 23, 2023
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
NYU Center for Data Science
AUGUST 24, 2023
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. It can also be approached in a variety of ways.
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Towards AI
APRIL 7, 2024
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? For example, it takes millions of images and runs them through a training algorithm.
Smart Data Collective
JUNE 4, 2021
Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Where to start? Reinforcement.
Smart Data Collective
AUGUST 24, 2022
Palakurla writes that random forest algorithms appear to be highly effective at gauging future cryptocurrency prices. Predictive analytics models with these algorithms can be useful for forecasting future bitcoin prices. These predictive analytics algorithms must evaluate events on a global scale, rather than those related to Albania.
Smart Data Collective
MARCH 9, 2023
As attackers use more self-learning algorithms to penetrate networks, static security postures have become obsolete. This method is in direct contrast to the typical security training program that relies on lectures or seminars delivered by security experts. So, what should companies do?
NYU Center for Data Science
NOVEMBER 26, 2024
This fieldwork informed Bhatt’s research on “algorithmic resignation” — the strategic withdrawal of AI systems in scenarios where human judgment better serves community values. His IEEE Computer paper “ When Should Algorithms Resign?
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