🍚 我要理解并注释这个项目.
更新路线图:
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第三章 ch03_Linear_Models_for_Regression.ipynb
测试文件: Kaiaicy-test-ch03.py
- 数据预处理/基模型回归中的基模型:
preprocess
子文件夹: - 3.1.1 Maximum likelihood and least squares:
- 3.1.4 Regularized least squares:
- 3.3 Bayesian Linear Regression:
- 3.5 The Evidence Approximation:
- 数据预处理/基模型回归中的基模型:
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第四章 ch04_Linear_Models_for_Classfication.ipynb
测试文件: Kaiaicy-test-ch04.py
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4.1.3 Least squares for classification:
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加入离群点, 与logistic回归模型比较:
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4.1.4 Fisher's linear discriminant:
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4.3.2 Logistic Regression:
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4.5 Bayesian Logistic Regression:
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Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
- python 3
- numpy
- scipy
- jupyter (optional: to run jupyter notebooks)
- matplotlib (optional: to plot results in the notebooks)
- sklearn (optional: to fetch data)
- ch1. Introduction
- ch2. Probability Distributions
- ch3. Linear Models for Regression
- ch4. Linear Models for Classification
- ch5. Neural Networks
- ch6. Kernel Methods
- ch7. Sparse Kernel Machines
- ch9. Mixture Models and EM
- ch10. Approximate Inference
- ch11. Sampling Methods
- ch12. Continuous Latent Variables