prior: videos

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1. 1▶HydroGym: A Reinforcement Learning Platform for Fluid DynamicsSteve BruntonNotes
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2. 2▶Convex SetsSteve BruntonNotes
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3. 3▶Convexity 101 [Optimization Bootcamp]Steve BruntonNotes
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4. 4▶Applications of OptimizationSteve BruntonNotes
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5. 5▶The Anatomy of an Optimization ProblemSteve BruntonNotes
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6. 6▶Optimization: A Bootcamp for Machine Learning, Inverse Problems, and ControlSteve BruntonNotes
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7. 7▶Bayesian Linear Regression [Python Example]Steve BruntonNotes
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8. 8▶Bayesian Linear Regression and Maximum a Posteriori (MAP) EstimateSteve BruntonNotes
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9. 9▶Bayesian Linear Regression and Maximum Likelihood EstimatesSteve BruntonNotes
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10. 10▶Monte Carlo Sampling and Bootstrapping in Bayesian InferenceSteve BruntonNotes
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11. 11▶Density Estimation with Gaussian Mixture Models (GMM) and Empirical PriorsSteve BruntonNotes
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12. 12▶Conjugate Priors Example: Normal Distribution and the Exponential Family of DistributionsSteve BruntonNotes
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13. 13▶Bayesian Updates and Conjugate PriorsSteve BruntonNotes
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14. 14▶Bayesian Inference: OverviewSteve BruntonNotes
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15. 15▶Properties of Chi-Squared and Student's t DistributionsSteve BruntonNotes
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16. 16▶Hypothesis Testing Revisited: Normal, t, and Chi-Squared Distribution TestsSteve BruntonNotes
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17. 17▶12 Prior Based Reconstruction IIIMachine Learning For Inverse GraphicsNotes
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18. 18▶7 Light TransportMachine Learning For Inverse GraphicsNotes
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19. 19▶20 How to give talksMachine Learning For Inverse GraphicsNotes
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20. 20▶11 Prior Based Reconstruction IIMachine Learning For Inverse GraphicsNotes
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21. 21▶8 Differentiable RenderingMachine Learning For Inverse GraphicsNotes
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22. 22▶6 Scene Representations III Neural Fields and Hybrid Discrete Neural Field RepresentationsMachine Learning For Inverse GraphicsNotes
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23. 23▶1 IntroductionMachine Learning For Inverse GraphicsNotes
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24. 24▶19 Representation Theory & SymmetriesMachine Learning For Inverse GraphicsNotes
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25. 25▶5 Scene Representations II Surface Representations and Discrete Field RepresentationsMachine Learning For Inverse GraphicsNotes
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26. 26▶4 Scene Representations I 2 5D and Monocular Depth PredictionMachine Learning For Inverse GraphicsNotes
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27. 27▶Lecture 11 - Computational Imaging | Digital Image ProcessingDigital Image ProcessingNotes
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28. 28▶Lecture 10 - Rethinking sensing & sampling | Digital Image ProcessingDigital Image ProcessingNotes
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29. 29▶Lecture 9 - Learning image priors | Digital Image ProcessingDigital Image ProcessingNotes
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30. 30▶Lecture 8 - Structured sparsity | Digital Image ProcessingDigital Image ProcessingNotes
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31. 31▶Lecture 7 - Sparsity-based priors | Digital Image ProcessingDigital Image ProcessingNotes
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32. 32▶Lecture 6 - Patch-based priors | Digital Image ProcessingDigital Image ProcessingNotes
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33. 33▶Lecture 5b Statistical Estimation and Inverse Problems | Digital Image ProcessingDigital Image ProcessingNotes
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34. 34▶Lecture 5a - Statistical Estimation and Inverse Problems | Digital Image ProcessingDigital Image ProcessingNotes
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35. 35▶Lecture 4 - Discrete Domain Signals and Systems | Digital Image ProcessingDigital Image ProcessingNotes
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36. 36▶Tutorial 3 | Digital Image ProcessingDigital Image ProcessingNotes
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