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Transforming Digital Learning: Learning Design Meets Service Design
Best Practices for Biomedical Research Data Management (HE)
Fundamentals of Reinforcement Learning
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Explore autonomous systems safety, from structured driving environments to unstructured scenarios, using failure identification and anomaly detection techniques for robust decision-making.
Explore innovative haptic modeling techniques using data-driven approaches for enhanced realism in touch-based interactions and social touch applications.
Modern ML systems sometimes undergo qualitative shifts in behavior simply by “scaling up” the number of parameters and training examples. Given this, how can we extrapolate the behavior of future ML systems and ensure that they behave safely and are alig…
Explore cognitively appropriate computing education for young learners through voice-based storytelling and visual tools, addressing literacy challenges and fostering computational thinking skills.
Explore continuous product discovery techniques, including customer engagement, opportunity solution trees, and effective decision-making for product teams to drive innovation and success.
I will describe some key ingredients to enable this: (1) robust self-supervised learning (2) uncertainty awareness (3) compositionality. We utilize NVIDIA Isaac for GPU-accelerated robot learning at scale on a variety of tasks and domains.
Explore AI ethics principles, their importance, and strategies to incorporate fairness, accountability, transparency, and human rights into AI development processes.
Explore visualization design spaces, their creation, assessment, and application in data representation. Insights on chart combinations, data wrangling, and task abstraction for effective visual communication.
Explore theories of visual inference, including uncertainty visualization, effect size judgments, and Bayesian cognition. Learn how visualizations support causal inference and model checks in data analysis.
Explore extended reality design challenges and solutions, empowering novice designers and instructors with accessible tools and methods for creating AR/VR experiences.
Explore maximum likelihood estimation in Bayesian networks, covering parameter learning, probabilistic inference, and advanced concepts like regularization and Expectation Maximization.
Explore advanced probabilistic inference techniques, including hidden Markov models, particle filtering, and Gibbs sampling, for AI applications in object tracking and more.
Explore Bayesian networks for AI, covering probabilistic inference, modeling, and applications in object tracking, language modeling, and document classification.
Learn to critically analyze statistics in news, understand risk communication, and navigate common pitfalls in data interpretation with Stanford professor Kristin Sainani's insightful webinar.
Explore computing with FPGAs, covering system architecture, performance examples, and programming techniques. Learn about stream architectures, arithmetic styles, and application domains for FPGA-based solutions.
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