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1. 1▶Uncertainty Modeling in AI | Lecture 6 (Part 1): Parameter learning with complete dataUncertainty Modeling in AI | National University of SingaporeNotes
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2. 2▶Uncertainty Modeling in AI | Lecture 5 (Part 4): Factor graph and the junction tree algorithmUncertainty Modeling in AI | National University of SingaporeNotes
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3. 3▶Uncertainty Modeling in AI | Lecture 5 (Part 3): Factor graph and the junction tree algorithmUncertainty Modeling in AI | National University of SingaporeNotes
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4. 4▶Uncertainty Modeling in AI | Lecture 5 (Part 2): Factor graph and the junction tree algorithmUncertainty Modeling in AI | National University of SingaporeNotes
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5. 5▶Uncertainty Modeling in AI | Lecture 5 (Part 1): Factor graph and the junction tree algorithmUncertainty Modeling in AI | National University of SingaporeNotes
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6. 6▶Uncertainty Modeling in AI | Lecture 4 (Part 2): Variable elimination and belief propagationUncertainty Modeling in AI | National University of SingaporeNotes
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7. 7▶Uncertainty Modeling in AI | Lecture 4 (Part 1): Variable elimination and belief propagationUncertainty Modeling in AI | National University of SingaporeNotes
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8. 8▶Uncertainty Modeling in AI | Lecture 3 (Part 3): Markov random Fields (Undirected graphical models)Uncertainty Modeling in AI | National University of SingaporeNotes
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9. 9▶Uncertainty Modeling in AI | Lecture 3 (Part 2): Markov random Fields (Undirected graphical models)Uncertainty Modeling in AI | National University of SingaporeNotes
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10. 10▶Uncertainty Modeling in AI | Lecture 3 (Part 1): Markov random Fields (Undirected graphical models)Uncertainty Modeling in AI | National University of SingaporeNotes
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11. 11▶Lecture 2.3 Gibbs Sampling | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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12. 12▶Lecture 2.4 MRF as ILP (I) | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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13. 13▶Lecture 2.5 MRF as ILP (II) | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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14. 14▶Lecture 2.6 Tree-Shaped MRF | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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15. 15▶Lecture 2.7 Belief Propagation | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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16. 16▶Lecture 2.8 Gaussian MRF (I) | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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17. 17▶Lecture 2.9 Gaussian MRF (II) | Undirected Probabilistic Graphical Models | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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18. 18▶Lecture 3.1 Perceptrons | Neural Networks | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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19. 19▶Lecture 3.2 Back Propagation | Neural Networks | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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20. 20▶Lecture 3.3 Introduction to Deep Learning | Neural Networks | MLCV 2017Machine Learning for Computer Vision class (Winter 2017-2018)Notes
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21. 21▶[MISS 2016] Nicholas Ayache - Anatomical/Physiological models in MI and biophysical simulation2016Medical Imaging Summer SchoolNotes
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22. 22▶[MISS 2016] Marleen de Bruijne - Learning imaging biomarkers: challenges and pitfalls2016Medical Imaging Summer SchoolNotes
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23. 23▶[MISS 2016] Ben Glocker - Solving continuous problems with discrete optimization2016Medical Imaging Summer SchoolNotes
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24. 24▶[MISS 2016] Alison Noble - Learning to interpret Ultrasound Imaging2016Medical Imaging Summer SchoolNotes
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25. 25▶[Miss 2016] Alison Noble - Popular Classics in Machine Learning for Medical Imaging2016Medical Imaging Summer SchoolNotes
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26. 26▶[MISS 2016] Carsten Rother - Introduction to Graphical Models2016Medical Imaging Summer SchoolNotes
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27. 27▶[MISS 2016] William M. Wells III - A Graphical Introduction to Probabilistic Graphical Models2016Medical Imaging Summer SchoolNotes
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28. 28▶[MISS 2016] William M. Wells III - Uncertainty in Registration with MCMC2016Medical Imaging Summer SchoolNotes
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29. 29▶[MISS 2016] William M. Wells III - A multi-perspective introduction to the EM algorithm2016Medical Imaging Summer SchoolNotes
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30. 30▶[MISS 2016] Carsten Rother - Graphical Models in BioImedical imaging2016Medical Imaging Summer SchoolNotes
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31. 31▶Bayesian Inference Part I - Zoubin Ghahramani - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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32. 32▶Bayesian Inference Part II - Zoubin Ghahramani - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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33. 33▶Bayesian Inference Part III - Zoubin Ghahramani - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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34. 34▶Bayesian Nonparametrics Part II - Tamara Broderick - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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35. 35▶Bayesian Nonparametrics Part III - Tamara Broderick - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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36. 36▶Bayesian Nonparametrics Part I - Tamara Broderick - MLSS 2015 TübingenProbabilistic Graphical ModelsNotes
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37. 37▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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38. 38▶Geoffrey Hinton: "Does the Brain do Inverse Graphics?"GSS2012: Deep Learning, Feature LearningNotes
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39. 39▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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40. 40▶Alan Yuille: "Compositional Models"GSS2012: Deep Learning, Feature LearningNotes
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41. 41▶Geoffrey Hinton: "A Computational Principle that Explains Sex, the Brain, and Sparse Coding"GSS2012: Deep Learning, Feature LearningNotes
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42. 42▶Yann LeCun: "Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction, Pt. 3"GSS2012: Deep Learning, Feature LearningNotes
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43. 43▶Geoffrey Hinton: "Some Applications of Deep Learning"GSS2012: Deep Learning, Feature LearningNotes
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44. 44▶Andrew Ng: "Non-linear Hypotheses, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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45. 45▶Andrew Ng: "Non-linear Hypotheses, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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46. 46▶Andrew Ng: "Advanced Topics + Research Philosophy / Neural Networks: Representation"GSS2012: Deep Learning, Feature LearningNotes
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47. 47▶Bayesian Inference 3 - Zoubin Ghahramani - MLSS 2013 TübingenProbabilistic Graphical ModelsNotes
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48. 48▶Bayesian Inference 1 - Zoubin Ghahramani - MLSS 2013 TübingenProbabilistic Graphical ModelsNotes
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49. 49▶Bayesian Inference 2 - Zoubin Ghahramani - MLSS 2013 TübingenProbabilistic Graphical ModelsNotes
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50. 50▶Bayesian Inference 1 - Zoubin Ghahramani - MLSS 2013 TübingenTheory of Statistical Machine LearningNotes
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