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pattern_recognition_and_ml_study's Introduction

Pattern Recognition and Machine learning Study

  • Summary and code implementation in weekly PRML seminar in SClab -> DAclub
  • Aug. 19, 2020 ~ Apr. 14, 2021

1. Polynomial Curve Fitting, Error function (RMS), Model Selection, Regularization

| Presentation |

2. Probability Theory - Bayesian probabilities, Parameter Estimation, Cross Validation, Information criteria - AIC

| Presentation |

3. The curse of dimensionality, Decision Theory, Minimizing the misclassification rate

| Presentation |

4. Minimizing the expected loss, Inference and decision, Generative and Disciriminative models, Regression

| Presentation |

5. Information theory, Entropy, Gaussian Distribution

| Presentation |

6. Binary Variables, beta distributions, multinomial variables, Gaussian Distribution

| Presentation |

Reference

[1] Pattern Recognition & Machine Learning, Bishop

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