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Greening the Economy: Sustainable Cities
Introduction to Graphic Illustration
Computational Social Science Methods
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Explore quantum mechanical computations, focusing on breaking dimensional constraints through analysis and probability. Learn about density functional theory and recent approaches to tackle existing issues.
Exploring challenges and opportunities in collecting and analyzing mobile and wearable data for human behavior insights, privacy-preserving techniques, and applications in health and urban computing.
Panel discussion exploring probabilistic numerical methods, covering topics like reference priors, robust uncertainty quantification, and Bayesian inversion methods for improved statistical inference and error quantification.
Exploring geophysical advancements: from analog instruments to digital data collection, satellite technology, and machine learning, revolutionizing Earth and planetary studies.
Explore geometric structures in data, from linear subspace models to nonlinear approaches, for efficient data analysis and representation in the era of big data.
Learn fundamental concepts and advanced techniques for analyzing time series data, including ARMA models, state-space models, and neural networks for forecasting and inference.
Exploring transparency in algorithmic decision-making, focusing on legal, ethical, and social implications of AI and automated systems in light of GDPR implementation and its impact on data protection.
Explore data protection challenges in public sector machine learning, focusing on fairness, accountability, and privacy-enhancing technologies. Gain insights into GDPR implications and potential solutions.
Explore legal protections against data-driven totalitarianism, examining concepts like consent, purpose limitation, and contestability in the context of AI and data processing regulations.
Explore climate data modeling in CMIP6, focusing on UK Earth System Model. Learn about processing pipelines, data conversion, and output analysis for advanced climate research.
Explore NASA's Earth observation data systems, statistical challenges in climate science, and innovative approaches to analyzing complex environmental datasets.
Explore climate data science through statistical analysis, modeling, and Earth observation techniques. Learn about climate systems, data representation, and challenges in Earth system science.
Learn fundamental concepts and advanced techniques in time series analysis, including ARMA models, state-space models, and neural networks for forecasting and inference.
Explore time series analysis, from moving average models to advanced state-space techniques and recurrent neural networks. Gain practical skills in parameter estimation, forecasting, and applying models to real-world data.
Explore cloud computing with Azure for faster, reproducible data science research. Hands-on labs cover deep learning, NLP, ML deployment, and more across various scientific disciplines.
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