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Uncommon Sense Teaching
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Explore bias in AI vision and language with Margaret Mitchell, focusing on ethical concerns, societal impacts, and strategies for developing more inclusive and fair artificial intelligence systems.
Explore a systems change approach for data science and AI solutions with Jake Porway, focusing on the Data for Good movement and leveraging technology for social impact.
Explore smart technologies enhancing browsing experiences through data visualization, interactive visualization, VR/AR, and visual analytics. Learn about human-machine communication and complex visualization techniques.
Explore the future of AI in finance with Samik Chandarana's keynote, covering data-driven decisions, deep neural nets, and AI applications in trading, payments, and natural language processing.
Explore machine learning techniques for analyzing CI/CD pipeline data, extracting insights, and automating failure identification using open-source tools like TensorFlow and Pandas with real-life OpenStack community data.
Explore dynamic price optimization using machine learning, from data analysis to cloud deployment. Learn about feature encoding, model selection, efficiency improvements, and practical implementation strategies for revenue growth.
Explore data-efficient solutions for supply chain management, focusing on forecasting with limited data and making robust decisions in complex, dynamic systems.
Explore trust and transparency in machine learning with insights on ethical AI development, open-source solutions, and IBM's approach to responsible AI implementation.
Explore Ray, a distributed execution engine for AI workloads. Learn about Tune for hyperparameter optimization and RLib for reinforcement learning, with insights on scalability and practical applications.
Discover techniques for optimizing Python performance, including efficient Pandas operations, Numba compilation, and Dask parallelization. Learn to identify bottlenecks and implement solutions for faster data processing and analysis.
Explore Explainable AI methods, applications, and developments with Dr. Samek. Learn about Layer-wise Relevance Propagation for various data types and neural architectures, and discuss challenges in the field.
Explore probabilistic deep learning in TensorFlow, focusing on Bayesian techniques, uncertainty modeling, and practical applications using TensorFlow Probability for more robust and interpretable neural networks.
Learn to create effective data visualizations using Python libraries Matplotlib and Bokeh. Explore design principles, reduce clutter, and add interactivity to communicate insights clearly and compellingly.
Renowned AI expert Michael Jordan discusses the future of machine learning, exploring potential billion-dollar industries and addressing key challenges in the field of data science and AI.
Learn to create publication-quality maps using R, combining public data sources to uncover interesting patterns. Covers geojson, shapefiles, Census data, geocoding, and data visualization principles.
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