Statistics And Data Analysis
In this course, we study some basic probility and sampling theories which are commonly used in machine learning.
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Syllabus
| Lecture | Key | Notes | Reading Material |
|---|---|---|---|
| 1. Statistics review | - Random Variable - pdf/cdf/pmf - Joint distribution - Expectation/covariance - Conditional expectation |
Probability theory Review | - Chernoff bound |
| 2. common distributions | - Discrete - Continuous |
Useful distribution Conjugate Prior Normal - Gamma Conjugate |
- expoential family |
| 3. sample and limit theory | - sampling mean and variance - order statistics distribution - weak and strong law of large numbers - central limit theorem |
sample and limit theory | |
| 4. T and F distribution | - distribution - distribution - distribution |
T and F distribution | |
| 5. Sampling | - Sample Random sampling - Confident interval - MCMC - Gibbs Sampling |
Survey Sampling | - 拒绝采样 - 别名采样 - MCMC |
| 6. Estimation of Parameters&Fitting of Probability Distribution | - MLE（parameter） - MLE for exponential family - Conjugate family （Bayes） - EM algorithm |
LDA思考与总结 | - 文本主题模型之LDA(一) LDA基础 |
| 7. Testing Hypotheses and assessing Goodness of fit | - Neyman-Perason lemma - Likelihood Ratio Tests |
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| 8. Testing and Summarizing | Testing and Summarizing) | ||
| 9. The analysis of Variance | The Analysis Of Variance | ||
| 10. notes for exam | Notes For Exam |