machine learning: videos

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1. 1▶Alex Damian - Understanding Optimization in Deep Learning with Central FlowsOne world theoretical machine learningNotes
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2. 2▶Minshuo Chen - Unlocking Adaptive Generative Decision-Making with Diffusion ModelsOne world theoretical machine learningNotes
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3. 3▶Tizian Wenzel: On the optimal shape parameter for kernel methods and beyondOne world theoretical machine learningNotes
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4. 4▶Hardening Digital Infrastructure: Two ExamplesStrachey Lectures1:02:07Notes
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5. 5▶Nicolas Boffi - Flow map language modelsOne world theoretical machine learningNotes
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6. 6▶This app is full of bugsJimmy's blogNotes
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7. 7▶This app is full of bugsJimmy's blogNotes
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8. 8▶Xiong Wang: Statistical learning problems in interacting particle systemsOne world theoretical machine learningNotes
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9. 9▶Giulio Biroli - Why Diffusion Models Don't MemorizeOne world theoretical machine learningNotes
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10. 10▶Pierre-Alexandre Mattei - Ensembles in machine learning: (simple) theory and (simple) practiceOne world theoretical machine learningNotes
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11. 11▶Yingzhen Li - Variational Uncertainty Decomposition for In-Context LearningOne world theoretical machine learningNotes
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12. 12▶An AI stack: from scaling AI workloads to evaluating LLMsStrachey Lectures55:58Notes
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13. 13▶Qing Qu - Understanding Generalization of Deep Generative Models based on Low-dimensional StructuresOne world theoretical machine learningNotes
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14. 14▶Andrew Ilersich - Learning Stochastic Multiscale Models of Spatiotemporal SystemsOne world theoretical machine learningNotes
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15. 15▶Advances in Garbled CircuitsStrachey Lectures48:12Notes
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16. 16▶Emoji phonetic alphabetJimmy's blogNotes
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17. 17▶Emoji phonetic alphabetJimmy's blogNotes
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18. 18▶Vibe doodlingJimmy's blogNotes
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19. 19▶Vibe doodlingJimmy's blogNotes
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20. 20▶Will Computers prove theorems?Strachey Lectures46:25Notes
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21. 21▶Formalizing the Future: Lean’s Impact on Mathematics, Programming, and AIStrachey Lectures47:14Notes
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22. 22▶Privacy, Verification, Robustness: A Cryptographer's perspective on MLStrachey Lectures1:04:18Notes
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23. 23▶From probabilistic bisimulation to representation learning via metricsStrachey Lectures55:03Notes
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24. 24▶Paris Perdikaris - PirateNets: Physics informed Deep Learning with Residual Adaptive NetworksPhysics-informed machine learning meets engineering seminar seriesNotes
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25. 25▶Tobias Heinrich Nagel - Kalman Bucy informed Neural Networks for System IdentificationPhysics-informed machine learning meets engineering seminar seriesNotes
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26. 26▶Sascha Ranftl - A Connection between Probability, Physics and Neural NetworkPhysics-informed machine learning meets engineering seminar seriesNotes
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27. 27▶Thomas Markovich - Quantum Diffusion Convolution Kernels on GraphsPhysics-informed machine learning meets engineering seminar seriesNotes
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28. 28▶Strachey Lecture: The Computer in the SkyStrachey Lectures1:02:09Notes
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29. 29▶AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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30. 30▶AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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31. 31▶AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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32. 32▶AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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33. 33▶AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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34. 34▶AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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35. 35▶Ameya Jagtap Enhancing Scientific Computing Through Physics informed Neural NetworksPhysics-informed machine learning meets engineering seminar seriesNotes
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36. 36▶Karan Taneja - Feature Encoded and Multi-Resolution Physics-Informed Machine Learning Approaches...Physics-informed machine learning meets engineering seminar seriesNotes
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37. 37▶AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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38. 38▶Lecture 12.1 - New Research Direction (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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39. 39▶Lecture 11.2 - Transference - Part2 (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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40. 40▶Lecture 9.2 - New Generative Models (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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41. 41▶Lecture 9.1 - Multimodal Generation (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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42. 42▶Lecture 7.2 - Multimodal Inference and Knowledge (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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43. 43▶Lecture 7.1 - Multimodal Interaction (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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44. 44▶Lecture 6.1 - Multimodal Transformers - Part2 (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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45. 45▶Lecture 5.2 - Structured Representations and Reasoning (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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46. 46▶Lecture 5.1 - Multimodal Transformers - Part1 (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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47. 47▶Lecture 4.2 - Aligned Representation (CMU Multimodal Machine Learning, Fall 2023)LP MorencyNotes
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48. 48▶Physics Informed Machine Learning: High Level Overview of AI and ML in Science and EngineeringPhysics Informed Machine LearningNotes
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49. 49▶Strachey Lecture: From classical to non-classical stochastic shortest path problemsStrachey Lectures57:09Notes
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50. 50▶Hongbo Zhao - Learning Battery Physics from ImagesPhysics-informed machine learning meets engineering seminar seriesNotes
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