Fuzzy Loss functions for GANs, Learning Analytics, Next Generation AI and Sustainability, Deep Learning for Melodic Frameworks
Speakers:
Prof. Priti S. Sajja, Sardar Patel University, India
Prof. Elvira Popescu, University of Craiova, Romania
Dr. Celestine Iwendi, University of Bolton, UK
Dr. Vishnu S. Pendyala, San Jose State University, USA
Date: Tuesday, July 12, 2022
L'International Journal of Educational Research Open a publié une étude bibliométrique sur le Learning Analytics (LA), ou Analyse de l’apprentissage. L'étude, qui s'étend de 2010 à 2020, permet d'établir le développement croissant de cette discipline. Elle croise les méthodes quantitatives traditionnelles dans l'éducation avec l'utilisation de mégadonnées, comme dans les domaines de l'Intelligence artificielle.
As data scientists and business leaders, we need to think about the ethical and privacy considerations of machine learning and artificial intelligence. FICO's Scott Zoldi shares recommendations around responsible AI, ethics, and privacy during this important conversation.
Join NYU-LEARN and Drs. Paul Prinsloo and Ravi Shroff in an engaging conversation on how we can productively navigate the intersection of learning analytics and equity and consider the role of educational data science as a tool for social justice.
Through 3 key case studies, Rachel Thomas covers how people can be harmed by machine learning gone wrong, why we as machine learning practitioners should care, and what tech ethics are.
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The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all.
Data science is a growing and promising discipline that has impacted various domains, including higher education. Owing to its ability to use precise methods and platforms to extract insights from data, several academic institutions are incorporating data science into their operations and educational curriculum.