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Machine Learning 1 - 2020

Machine Learning 1 - 2020

Erik Bekkers via YouTube Direct link

3.3 Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020)

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3.3 Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020)

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Machine Learning 1 - 2020

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  1. 1 1.2 What Is Machine Learning (UvA - Machine Learning 1 - 2020)
  2. 2 1.3 Types Of Machine Learning (UvA - Machine Learning 1 - 2020)
  3. 3 1.4 Probability Theory Bayes (UvA - Machine Learning 1 - 2020)
  4. 4 1.5 Probability Theory: Example (UvA - Machine Learning 1 - 2020)
  5. 5 2.1 Expectation Variance (UvA - Machine Learning 1 - 2020)
  6. 6 2.2 Gaussian (UvA - Machine Learning 1 - 2020)
  7. 7 2.3 Maximum Likelihood (UvA - Machine Learning 1 - 2020)
  8. 8 2.4 Maximum Likelihood: Example (UvA - Machine Learning 1 - 2020)
  9. 9 2.5 Maximum A Posteriori (UvA - Machine Learning 1 - 2020)
  10. 10 2.6 Bayesian Prediction (UvA - Machine Learning 1 - 2020)
  11. 11 3.1 Linear Regression With Basis Functions (UvA - Machine Learning 1 - 2020)
  12. 12 3.2 Linear Regression Via Maximum Likelihood (UvA - Machine Learning 1 - 2020)
  13. 13 3.3 Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020)
  14. 14 3.4 Underfitting Overfitting (UvA - Machine Learning 1 - 2020)
  15. 15 3.5 Regularized Least Squares (UvA - Machine Learning 1 - 2020)
  16. 16 4.1 Model Selection (UvA - Machine Learning 1 - 2020)
  17. 17 4.2 Bias Variance Decomposition (UvA - Machine Learning 1 - 2020)
  18. 18 4.3 Gaussian Posteriors (UvA - Machine Learning 1 - 2020)
  19. 19 4.4 Sequential Bayesian Learning (UvA - Machine Learning 1 - 2020)
  20. 20 4.5 Bayesian Predictive Distributions (UvA - Machine Learning 1 - 2020)
  21. 21 5.1 Equivalent Kernel (UvA - Machine Learning 1 - 2020)
  22. 22 5.2 Bayesian Model Comparison (UvA - Machine Learning 1 - 2020)
  23. 23 5.3 Model Evidence Approximation and Empirical Bayes (UvA - Machine Learning 1 - 2020)
  24. 24 5.4 Classification With Decision Regions (UvA - Machine Learning 1 - 2020)
  25. 25 5.5 Decision Theory (UvA - Machine Learning 1 - 2020)
  26. 26 5.6 Probabilistic Generative Models (UvA - Machine Learning 1 - 2020)
  27. 27 6.1 Probabilistic Generative Modeling: Maximum Likelihood (UvA - Machine Learning 1 - 2020)
  28. 28 6.2 Probabilistic Generative Modeling: Discrete Data (UvA - Machine Learning 1 - 2020)
  29. 29 6.3 Discriminant Functions (UvA - Machine Learning 1 - 2020)
  30. 30 6.4 Discriminant Functions: Least Squares Regression (UvA - Machine Learning 1 - 2020)
  31. 31 6.5 Discriminant Functions: The Perceptron (UvA - Machine Learning 1 - 2020)
  32. 32 7.1 Classification With Basis Functions (UvA - Machine Learning 1 - 2020)
  33. 33 7.2 Probabilistic Discriminative Models: Logistic Regression (UvA - Machine Learning 1 - 2020)
  34. 34 7.3 Logistic Regression: Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020)
  35. 35 7.4 Logistic Regression: Newton Raphson (UvA - Machine Learning 1 - 2020)
  36. 36 8.1 Neural Networks (UvA - Machine Learning 1 - 2020)
  37. 37 8.2 Neural Networks: Universal Approximation Theorem (UvA - Machine Learning 1 - 2020)
  38. 38 8.3 Neural Networks: Losses (UvA - Machine Learning 1 - 2020)
  39. 39 8.4 Neural Networks: Stochastic Gradient Descent (UvA - Machine Learning 1 - 2020)
  40. 40 8.5 Neural Networks: Backpropagation (UvA - Machine Learning 1 - 2020)
  41. 41 9.1 Unsupervised Learning: Latent Variable Models (UvA - Machine Learning 1 - 2020)
  42. 42 9.2 K-Means Clustering (UvA - Machine Learning 1 - 2020)
  43. 43 9.3 Intermezzo: Lagrange Multipliers (UvA - Machine Learning 1 - 2020)
  44. 44 9.4 Gaussian Mixture Models And Expectation Maximization (UvA - Machine Learning 1 - 2020)
  45. 45 10.1 Principal Component Analysis: Maximum Variance (UvA - Machine Learning 1 - 2020)
  46. 46 10.2 Principal Component Analysis: Minimal Reconstruction Error (UvA - Machine Learning 1 - 2020)
  47. 47 10.3 Probabilistic Principal Component Analysis (UvA - Machine Learning 1 - 2020)
  48. 48 10.4 Non-Linear Principal Component Analysis (UvA - Machine Learning 1 - 2020)
  49. 49 11.1 Kernelizing Linear Models (UvA - Machine Learning 1 - 2020)
  50. 50 11.2 The Kernel Trick (UvA - Machine Learning 1 - 2020)
  51. 51 11.3 Support Vector Machines: Maximum Margin Classifiers (UvA - Machine Learning 1 - 2020)
  52. 52 11.4 Intermezzo: Inequality Constraint Optimization (UvA - Machine Learning 1 - 2020)
  53. 53 11.5 Support Vector Machines: Kernel SVM (UvA - Machine Learning 1 - 2020)
  54. 54 11.6 Support Vector Machines: Soft-Margin Classifiers (UvA - Machine Learning 1 - 2020)
  55. 55 12.1 Some Properties Of Gaussian Distributions (UvA - Machine Learning 1 - 2020)
  56. 56 12.2 Kernelizing Bayesian Regression (UvA - Machine Learning 1 - 2020)
  57. 57 12.3 Gaussian Processes (UvA - Machine Learning 1 - 2020)
  58. 58 12.4 Gaussian Processes With An Exponential Kernel (UvA - Machine Learning 1 - 2020)
  59. 59 12.5 Gaussian Processes: Regression (UvA - Machine Learning 1 - 2020)
  60. 60 13.1 Model Combination Methods Vs Bayesian Model Averaging (UvA - Machine Learning 1 - 2020)
  61. 61 13.2 Bootstrapping And Feature Bagging (UvA - Machine Learning 1 - 2020)
  62. 62 13.3 Boosting (UvA - Machine Learning 1 - 2020)
  63. 63 13.4 Decision Trees And Random Forests (UvA - Machine Learning 1 - 2020)

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