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How Things Work: An Introduction to Physics
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Exploring the necessity of memorization in learning algorithms, this talk delves into privacy concerns, long-tail distributions, and the role of memorization in achieving optimal generalization error in deep learning.
Explore unsupervised learning in generative art, reinforcement learning agents as image creators, and the potential of AI in artistic tools and creative processes.
Explore machine learning techniques for better decision-making, including ROC curves, calibration methods, and precision-recall-gain curves. Learn to adapt models to deployment contexts and measure ML performance.
Explore Spotify's approach to personalizing music recommendations using machine learning, focusing on user intents, relevance, diversity, and satisfaction to enhance the listening experience.
Exploring challenges in simulation models for data science, focusing on parameter estimation and initialization. Discusses fusion with machine learning and potential impact on policy analysis and forecasting.
Explore reinforcement learning and mean-field games in algorithmic trading, covering deep Q-learning, reinforced deep Kalman filters, and heterogeneous agent interactions with differing beliefs in financial markets.
Explore the ethical implications of AI optimization strategies and their potential for unintended consequences in financial markets and AI regulation.
Exploring how precise language enhances statistical understanding for teachers, students, and citizens. Emphasizes interpretation over calculation, addressing common misunderstandings and the importance of clear communication in statistics.
Explore the challenges of balancing generalization and individual justice in AI decision-making, focusing on explainability, limitations, and ethical implications in real-world contexts.
Explore the ethical implications and limitations of AI explainability, focusing on individual justice, societal contexts, and human interpretation in algorithmically supported decision-making.
Explore uncertainty quantification using Gaussian Process emulators for complex systems. Learn about sequential design methods, mutual information criteria, and applications in climate, tsunami, and earthquake modeling.
Explore efficient methods for emulating complex computer models using Known Boundary Emulation, focusing on uncertainty quantification and applications in climate, tsunami, and earthquake problems.
Explore deep Gaussian processes for Bayesian inversion, covering theoretical foundations, numerical methods, and applications in uncertainty quantification for complex physical systems.
Explore pragmatic approaches to global temperature reconstruction using multiscale models, data sources, and statistical techniques for uncertainty quantification and bias correction.
Explore state-building, identity, and digital technology's impact on power dynamics and citizen experiences in modern governance systems.
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