{"id":13713488,"url":"https://github.com/lijin-THU/notes-machine-learning","last_synced_at":"2025-05-06T23:32:16.237Z","repository":{"id":109150304,"uuid":"53230112","full_name":"lijin-THU/notes-machine-learning","owner":"lijin-THU","description":"鉴于我没有时间继续写这个东西，这个项目暂时废止","archived":false,"fork":false,"pushed_at":"2017-03-24T13:03:52.000Z","size":1602,"stargazers_count":835,"open_issues_count":0,"forks_count":317,"subscribers_count":93,"default_branch":"master","last_synced_at":"2024-11-09T05:10:02.253Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lijin-THU.png","metadata":{"files":{"readme":"ReadMe.ipynb","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-03-06T00:29:20.000Z","updated_at":"2024-11-06T01:29:50.000Z","dependencies_parsed_at":"2023-03-13T14:15:45.591Z","dependency_job_id":null,"html_url":"https://github.com/lijin-THU/notes-machine-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lijin-THU%2Fnotes-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lijin-THU%2Fnotes-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lijin-THU%2Fnotes-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lijin-THU%2Fnotes-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lijin-THU","download_url":"https://codeload.github.com/lijin-THU/notes-machine-learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224547003,"owners_count":17329413,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T23:01:37.636Z","updated_at":"2024-11-14T00:30:38.927Z","avatar_url":"https://github.com/lijin-THU.png","language":"Jupyter Notebook","readme":"{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 机器学习笔记\\n\",\n    \"\\n\",\n    \"## 简介\\n\",\n    \"\\n\",\n    \"\u003e 作者：李金 \u003cbr\u003e\\n\",\n    \"\u003e 版本：0.0.1\u003cbr\u003e\\n\",\n    \"\u003e 邮件：lijinwithyou@gmail.com\\n\",\n    \"\\n\",\n    \"机器学习笔记，使用 `jupyter notebook (ipython notebook)` 进行展示。\\n\",\n    \"\\n\",\n    \"`Github` 加载 `.ipynb` 的速度较慢，建议在 [Nbviewer](http://nbviewer.jupyter.org/github/lijin-THU/notes-machine-learning/blob/master/ReadMe.ipynb) 中查看该项目。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"----\\n\",\n    \"\\n\",\n    \"## 目录\\n\",\n    \"\\n\",\n    \"第一部分来自 `Bishop` 的经典书籍 `Pattern Recognition and Machine Learning`。\\n\",\n    \"\\n\",\n    \"第二部分来自 `Bengio` 的最新书籍 `Deep Learning`。\\n\",\n    \"\\n\",\n    \"### 第一部分 PRML 笔记\\n\",\n    \"\\n\",\n    \"- [1. 简介](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction)\\n\",\n    \"    - [1.1. 例子：多项式拟合](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-01-Example-Polynomial-Curve-Fitting.ipynb)\\n\",\n    \"    - [1.2. 概率论](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb)\\n\",\n    \"        - [1.2.1. 概率密度函数](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.1-概率密度函数)\\n\",\n    \"        - [1.2.2. 期望和方差](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.2-期望和方差)\\n\",\n    \"        - [1.2.3. Bayes 概率](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.3-Bayes-概率)\\n\",\n    \"        - [1.2.4. 高斯分布](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.4-高斯分布)\\n\",\n    \"        - [1.2.5. 重新理解曲线拟合](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.5-重新理解曲线拟合)\\n\",\n    \"        - [1.2.6. Bayes 曲线拟合](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-02-Probability-Theory.ipynb#1.2.6-Bayes-曲线拟合)\\n\",\n    \"    - [1.3. 模型选择](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-03-Model-Selection.ipynb)\\n\",\n    \"    - [1.4. 维数灾难](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-04-The-Curse-of-Dimensionality.ipynb)\\n\",\n    \"    - [1.5. 决策理论](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb)\\n\",\n    \"        - [1.5.1. 最小错误率决策](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb#1.5.1-最小错误率决策)\\n\",\n    \"        - [1.5.2. 最小风险决策](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb#1.5.2-最小风险决策)\\n\",\n    \"        - [1.5.3. 拒绝选项](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb#1.5.3-拒绝选项)\\n\",\n    \"        - [1.5.4. 推断和决策](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb#1.5.4-推断和决策)\\n\",\n    \"        - [1.5.5. 回归问题的损失函数](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-05-Decision-Theory.ipynb#1.5.5-回归问题的损失函数)\\n\",\n    \"    - [附录 D 变分法](Pattern-Recognition-and-Machine-Learning/Appendix/Appendix-D-Calculus-of-Variations.ipynb)\\n\",\n    \"    - [1.6. 信息论](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-06-Information-Theory.ipynb)\\n\",\n    \"        - [1.6.1. 相对熵和互信息](Pattern-Recognition-and-Machine-Learning/Chap-01-Introduction/01-06-Information-Theory.ipynb#1.6.1-相对熵和互信息)\\n\",\n    \"    - [附录 E Lagrange 乘子](Pattern-Recognition-and-Machine-Learning/Appendix/Appendix-E-Lagrange-Multipliers.ipynb)\\n\",\n    \"- [2. 概率分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions)\\n\",\n    \"    - [2.1. 二元变量](PRML/Chap-02-Probability-Distributions/02-01-Binary-Variables.ipynb)\\n\",\n    \"        - [2.1.1. Beta 分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-01-Binary-Variables.ipynb#2.1.1-Beta-分布)\\n\",\n    \"    - [2.2. 多元变量](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-02-Multinomial-Variables.ipynb)\\n\",\n    \"        - [2.2.1. 狄利克雷分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-02-Multinomial-Variables.ipynb#2.2.1-狄利克雷分布)\\n\",\n    \"    - [2.3. 高斯分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb)\\n\",\n    \"        - [2.3.1. 条件高斯分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.1-条件高斯分布)\\n\",\n    \"        - [2.3.2. 边缘高斯分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.2-边缘高斯分布)\\n\",\n    \"        - [2.3.3. 高斯变量的贝叶斯理论](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.3-高斯变量的贝叶斯理论)\\n\",\n    \"        - [2.3.4. 高斯分布最大似然](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.4-高斯分布最大似然)\\n\",\n    \"        - [2.3.5. 序列估计](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.5-序列估计)\\n\",\n    \"        - [2.3.6. 高斯分布的贝叶斯估计](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.6-高斯分布的贝叶斯估计)\\n\",\n    \"        - [2.3.7. 学生 t 分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.7-学生-t-分布)\\n\",\n    \"        - [2.3.8. 周期变量和 von Mises 分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.8-周期变量和-von-Mises-分布)\\n\",\n    \"        - [2.3.9. 高斯混合模型](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-03-The-Gaussian-Distribution.ipynb#2.3.9-高斯混合模型)\\n\",\n    \"    - [2.4. 指数族分布](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-04-The-Exponential-Family.ipynb)\\n\",\n    \"        - [2.4.1. 最大似然和充分统计量](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-04-The-Exponential-Family.ipynb#2.4.1-最大似然和充分统计量)\\n\",\n    \"        - [2.4.2. 共轭先验](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-04-The-Exponential-Family.ipynb#2.4.2-共轭先验)\\n\",\n    \"        - [2.4.3. 无信息先验](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-04-The-Exponential-Family.ipynb#2.4.3-无信息先验)\\n\",\n    \"    - [2.5. 非参数方法](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-05-Nonparametric-Methods.ipynb#2.5 非参数方法)\\n\",\n    \"        - [2.5.1. 核密度估计量](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-05-Nonparametric-Methods.ipynb#2.5.1-核密度估计量)\\n\",\n    \"        - [2.5.2. 近邻方法](Pattern-Recognition-and-Machine-Learning/Chap-02-Probability-Distributions/02-05-Nonparametric-Methods.ipynb#2.5.2-近邻方法)\\n\",\n    \"- [3. 线性回归模型](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression)        \\n\",\n    \"    - [3.1. 线性基函数回归模型](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb)\\n\",\n    \"        - [3.1.1. 最大似然和最小二乘](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb#3.1.1-最大似然和最小二乘)\\n\",\n    \"        - [3.1.2. 最小二乘的几何表示](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb#3.1.2-最小二乘的几何表示)\\n\",\n    \"        - [3.1.3. 序贯学习](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb#3.1.3-序贯学习)\\n\",\n    \"        - [3.1.4. 带正则的最小二乘](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb#3.1.4-带正则的最小二乘)\\n\",\n    \"        - [3.1.5. 多维输出](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-01-Linear-Basis-Function-Models.ipynb#3.1.5-多维输出)\\n\",\n    \"    - [3.2 Bias-Variance 分解](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-02-The-Bias-Variance-Decomposition.ipynb)\\n\",\n    \"    - [3.3 Bayes 线性回归](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-03-Bayesian-Linear-Regression.ipynb)\\n\",\n    \"        - [3.3.1. 参数的分布](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-03-Bayesian-Linear-Regression.ipynb#3.3.1-参数的分布)\\n\",\n    \"        - [3.3.2. 预测值的分布](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-03-Bayesian-Linear-Regression.ipynb#3.3.2-预测值的分布)\\n\",\n    \"        - [3.3.3. 等价核](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-03-Bayesian-Linear-Regression.ipynb#3.3.3-等价核)\\n\",\n    \"    - [3.4 贝叶斯模型的比较](Pattern-Recognition-and-Machine-Learning/Chap-03-Linear-Models-for-Regression/03-04-Bayesian-Model-Comparison.ipynb)\\n\",\n    \"- [4. 线性分类模型](Pattern-Recognition-and-Machine-Learning/Chap-04-Linear-Models-for-Classification)\\n\",\n    \"    - [4.1 判别函数](Pattern-Recognition-and-Machine-Learning/Chap-04-Linear-Models-for-Classification/04-01-Discriminant-Functions.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 第二部分 DP 笔记\\n\",\n    \"\\n\",\n    \"- [I 数学和机器学习基础](Deep-Learning/Part-I)\\n\",\n    \"    - [2. 线性代数](Deep-Learning/Part-I/Chap-02-Linear-Algebra)\\n\",\n    \"        - [2.1 标量，向量，矩阵和张量](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-01-Scalars-Vectors-Matrices-and-Tensors.ipynb)\\n\",\n    \"        - [2.2 矩阵乘法](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-02-Multiplying-Matrices-and-Vectors.ipynb)\\n\",\n    \"        - [2.2 单位矩阵和逆](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-03-Identity-and-Inverse-Matrices.ipynb)\\n\",\n    \"        - [2.4 线性无关和生成空间](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-04-Linear-Dependence-and-Span.ipynb)\\n\",\n    \"        - [2.5 范数](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-05-Norms.ipynb)\\n\",\n    \"        - [2.6 特殊矩阵和向量](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-06-Special-Kinds-of-Matrices-and-Vectors.ipynb)\\n\",\n    \"        - [2.7 特征值分解](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-07-Eigendecomposition.ipynb)\\n\",\n    \"        - [2.8 奇异值分解](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-08-Singular-Value-Decomposition.ipynb)\\n\",\n    \"        - [2.9 Moore-Penrose 伪逆](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-09-The-Moore-Penrose-Pseudoinverse.ipynb)\\n\",\n    \"        - [2.10 矩阵的迹](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-10-The-Trace-Operator.ipynb)\\n\",\n    \"        - [2.11 行列式](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-11-The-Determinant.ipynb)\\n\",\n    \"        - [2.12 例子：主成分分析](Deep-Learning/Part-I/Chap-02-Linear-Algebra/02-12-Example-Principal-Components-Analysis.ipynb)\\n\",\n    \"\\n\",\n    \"----\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 参考资料和文献：\\n\",\n    \"\\n\",\n    \"[1] Christopher, M. Bishop. \\\"Pattern recognition and machine learning.\\\" Company New York 16.4 (2006): 049901.\\n\",\n    \"\\n\",\n    \"[2] Goodfellow I, Bengio Y, Courville A. Deep learning[J]. 2015, 2016.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijin-THU%2Fnotes-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flijin-THU%2Fnotes-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijin-THU%2Fnotes-machine-learning/lists"}