linear model: videos

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1. 1▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 16: reviewUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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2. 2▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 15: linear mixed modelUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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3. 3▶STATS 205 - Hierarchical Linear Models - Lecture 14: zero-inflated count regression; random effectsUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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4. 4▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 13: quasi-Poisson/neg binomial regressionUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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5. 5▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 12 (binary response, Poisson regression)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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6. 6▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 11 (score, LRT, Wald test; GLM inference)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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7. 7▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 10 (IRLS; goodness of fit of GLM)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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8. 8▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 9 (Iteratively Reweighted Least Squares)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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9. 9▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 8 (Newton-Raphson; Fisher scoring)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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10. 10▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 7 (GLM exponential family)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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11. 11▶Linear Models -- Spring 2023 -- Lecture 16Linear Models -- Spring 2023Notes
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12. 12▶Linear Models -- Spring 2023 -- Lecture 15Linear Models -- Spring 2023Notes
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13. 13▶Linear Models -- Spring 2023 -- Lecture 14Linear Models -- Spring 2023Notes
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14. 14▶Linear Models -- Spring 2023 -- Lecture 13Linear Models -- Spring 2023Notes
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15. 15▶Linear Models -- Spring 2023 -- Lecture 12Linear Models -- Spring 2023Notes
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16. 16▶Linear Models -- Spring 2023 -- Lecture 11Linear Models -- Spring 2023Notes
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17. 17▶Linear Models -- Spring 2023 -- Lecture 9Linear Models -- Spring 2023Notes
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18. 18▶Linear Models -- Spring 2023 -- Lecture 8Linear Models -- Spring 2023Notes
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19. 19▶Linear Models -- Spring 2023 -- Lecture 7Linear Models -- Spring 2023Notes
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20. 20▶Linear Models -- Spring 2023 -- Lecture 6Linear Models -- Spring 2023Notes
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21. 21▶Channel Intro - Applied Machine LearningApplied Machine Learning 2020Notes
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22. 22▶Applied ML 2020 - 14 - Clustering and Mixture ModelsApplied Machine Learning 2020Notes
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23. 23▶Applied ML 2020 - 13 - Dimensionality reductionApplied Machine Learning 2020Notes
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24. 24▶Applied ML 2020 - 12 - AutoML (plus some feature selection)Applied Machine Learning 2020Notes
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25. 25▶Applied ML 2020 - 11 - Model Inspection and Feature SelectionApplied Machine Learning 2020Notes
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26. 26▶Applied ML 2020 - 10 - Calibration, Imbalanced dataApplied Machine Learning 2020Notes
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27. 27▶Applied ML 2020 - 09 - Model Evaluation and MetricsApplied Machine Learning 2020Notes
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28. 28▶Applied ML 2020 - 08 - Gradient BoostingApplied Machine Learning 2020Notes
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29. 29▶Applied ML 2020 - 07 - Decision Trees and Random ForestsApplied Machine Learning 2020Notes
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30. 30▶Applied ML 2020 - 06 - Linear Models for ClassificationApplied Machine Learning 2020Notes
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31. 31▶Applied Machine Learning 2019 - Lecture 15 - Clustering and Mixture modelsApplied Machine Learning - Spring 2019Notes
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32. 32▶Applied Machine Learning 2019 - Lecture 14 - Dimensionality ReductionApplied Machine Learning - Spring 2019Notes
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33. 33▶Applied Machine Learning 2019 - Lecture 13 - Parameter Selection and Automatic Machine LearningApplied Machine Learning - Spring 2019Notes
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34. 34▶Applied Machine Learning 2019 - Lecture 12 - Model Interpretration and Feature SelectionApplied Machine Learning - Spring 2019Notes
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35. 35▶Applied Machine Learning 2019 - Lecture 11 - Imbalanced dataApplied Machine Learning - Spring 2019Notes
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36. 36▶Applied Machine Learning 2019 - Lecture 10 - Model EvaluationApplied Machine Learning - Spring 2019Notes
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37. 37▶Applied Machine Learning 2019 - Lecture 09 - Gradient boosting; CalibrationApplied Machine Learning - Spring 2019Notes
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38. 38▶Applied Machine Learning 2019 - Lecture 08 - Trees, Forests and EnsemblesApplied Machine Learning - Spring 2019Notes
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39. 39▶Applied Machine Learning 2019 - Lecture 07 - Linear Models for Classifications, SVMsApplied Machine Learning - Spring 2019Notes
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40. 40▶Applied Machine Learning 2019 - Lecture 06 - Linear Models for RegressionApplied Machine Learning - Spring 2019Notes
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41. 41▶14 Review and outlook (MLVU2018)VU Machine Learning 2018Notes
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42. 42▶13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo (MLVU2018)VU Machine Learning 2018Notes
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43. 43▶12 Matrix Models: Recommender systems and Matrix factorization (MLVU2018)VU Machine Learning 2018Notes
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44. 44▶11 Models for Sequential Data: Markov Models, Word2Vec, RNNs and LSTMs.VU Machine Learning 2018Notes
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45. 45▶10 Tree Models and Ensembles: Decision Trees, Boosting, Bagging, Gradient Boosting (MLVU2018)VU Machine Learning 2018Notes
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46. 46▶09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)VU Machine Learning 2018Notes
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47. 47▶08 Probabilistic Models 2, Normal Distributions, Gaussian Mixtures and EM (MLVU2018)VU Machine Learning 2018Notes
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48. 48▶07 Linear Models 2: Support Vector Machines, the Kernel trick (MLVU2018)VU Machine Learning 2018Notes
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49. 49▶06 Deep Learning 1: Neural networks, Convolutional layers (MLVU2018)VU Machine Learning 2018Notes
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50. 50▶05 Probabilistic Models 1: Naive Bayes, Entropy, Logistic Regression (MLVU2018)VU Machine Learning 2018Notes
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