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1. 1▶Cooked tomato sauce | part oneSupertastersNotes
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2. 2▶Context-free grammars, part 1Computational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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3. 3▶Multiple center embedding, the pumping lemma, and limitations of finite-state automataComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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4. 4▶Finite-state transducersComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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5. 5▶Introduction to psycholinguistic methods, part 3: readingComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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6. 6▶Introduction to psycholinguistic methods, part 4: neural methodsComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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7. 7▶Introduction to psycholinguistic methods, part 2: the visual world paradigmComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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8. 8▶Introduction to psycholinguistic methods, part 1Computational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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9. 9▶Finite-state models, regular languages, English syntax, and strong vs. weak generative capacityComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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10. 10▶Regular expressions and their relation with finite-state modelsComputational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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11. 11▶Introductory language models, part 1Computational Psycholinguistics – MIT (Undergraduate/Graduate course)Notes
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12. 12▶1.1 IntroductionCombinatorial OptimizationNotes
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13. 13▶2.4 The Simplex Method, Part IIICombinatorial OptimizationNotes
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14. 14▶3.1 LP Duality, Part ICombinatorial OptimizationNotes
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15. 15▶3.4 The Primal Dual Framework, Part ICombinatorial OptimizationNotes
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16. 16▶3.5 The Primal Dual Framework, Part IICombinatorial OptimizationNotes
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17. 17▶2.3 The Simplex Method, Part IICombinatorial OptimizationNotes
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18. 18▶4.4 Ford Fulkerson, Part ICombinatorial OptimizationNotes
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19. 19▶4.5 Ford Fulkerson, Part IICombinatorial OptimizationNotes
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20. 20▶3.3 LP Duality, Part IIICombinatorial OptimizationNotes
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21. 21▶4.2 Primal Dual Applied to Shortest PathCombinatorial OptimizationNotes
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22. 22▶Software Engineering - Lecture 07 - Design Patterns - Part 2Software Engineering LecturesNotes
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23. 23▶Software Engineering - Lecture 07 - Design Patterns - Part 1Software Engineering LecturesNotes
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24. 24▶Software Engineering - Lecture 06 - Design Patterns - Part 2Software Engineering LecturesNotes
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25. 25▶Software Engineering - Lecture 06 - Design Patterns - Part 1Software Engineering LecturesNotes
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26. 26▶Software Engineering - Lecture 05 - Agile Methods - Part 3Software Engineering LecturesNotes
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27. 27▶Software Engineering - Lecture 05 - Agile Methods - Part 2Software Engineering LecturesNotes
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28. 28▶Software Engineering - Lecture 05 - Agile Methods - Part 1Software Engineering LecturesNotes
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29. 29▶Software Engineering - Lecture 04 - Software Processes - Part 2Software Engineering LecturesNotes
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30. 30▶Software Engineering - Lecture 04 - Software Processes - Part 1Software Engineering LecturesNotes
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31. 31▶Software Engineering - Lecture 03 - Part 2 - TestingSoftware Engineering LecturesNotes
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32. 32▶Software Engineering - Lecture 03 - Part 1 - UMLSoftware Engineering LecturesNotes
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33. 33▶Software Engineering - Lecture 02 - Part 2 - OOPSoftware Engineering LecturesNotes
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34. 34▶Software Engineering - Lecture 02 - Part 1 - git & RCSSoftware Engineering LecturesNotes
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35. 35▶Software Engineering - Lecture 01 - IntroductionSoftware Engineering LecturesNotes
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36. 36▶Lecture 01 IntroductionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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37. 37▶Lecture 02 Markov Decision ProcessesCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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38. 38▶Lecture 03 Solving known MDPsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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39. 39▶Lecture 04 Solving Known MDPsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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40. 40▶Lecture 05 Monte Carlo MethodsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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41. 41▶Lecture 06 Temporal Difference MethodCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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42. 42▶Lecture 07 Neural Networks Architectures for RLCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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43. 43▶Lecture 08 Function Approximation for PredictionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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44. 44▶Lecture 09 Value FunctionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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45. 45▶Lecture 10 Policy Gradient MethodsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
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46. 46▶What is Machine Learning - Bernhard Schölkopf - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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47. 47▶Learning Digital Humans by Capturing Real Ones - Michael Black - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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48. 48▶Implicit Generative Models - Ilya Tolstikhin - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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49. 49▶Reinforcement Learning - Jan Peters - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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50. 50▶Marrying Graphical Models & Deep Learning - Max Welling - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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