lecture concentration

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1. 1▶Lecture 15: Minimax theorySpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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2. 2▶Lecture 14: Concentration of measureSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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3. 3▶Lecture 13: Concentration of MeasureSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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4. 4▶Lecture 12: High Dimensional TestingSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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5. 5▶Lecture 11: ClusteringSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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6. 6▶Lecture 10: ClusteringSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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7. 7▶Lecture 09: ClusteringSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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8. 8▶Lecture 08: Density estimationSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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9. 9▶Lecture 07: RKHSSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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10. 10▶Lecture 06: Nonparametric classificationSpring 2017: Statistical Machine Learning (10-702/36-702)Notes
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11. 11▶Lecture 15: ClusteringSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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12. 12▶Lecture 14: BoostingSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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13. 13▶Lecture 13: Nonparametric BayesSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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14. 14▶Lecture 12: Minimax TheorySpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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15. 15▶Lecture 11: Minimax TheorySpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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16. 16▶Lecture 10: Nonparametric ClassificationSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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17. 17▶Lecture 09: Linear ClassificationSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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18. 18▶Lecture 08: Trend filteringSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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19. 19▶Lecture 07: Nonparametric RegressionSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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20. 20▶Lecture 06: Nonparametric RegressionSpring 2016: Statistical Machine Learning (10-702/36-702)Notes
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