Machine Learning Notes === --- - [README](https://hackmd.io/@pipibear/SyoyR5CVR) --- Ch1: Introduction --- - [1.1 What is Machine Learning?](https://hackmd.io/@pipibear/Bk5-l-VNA) - [1.2 Examples of Machine Learning Applications](https://hackmd.io/@pipibear/SJWE2r44C) - [1.3 Notes](https://hackmd.io/@pipibear/Sk-HaHE4R) --- Ch2: Supervised Learning --- - [2.1 Learning a Class from Examples](https://hackmd.io/@pipibear/HJHdW-E40) - [2.2 Vapnik-Chervonenkis Dimension](https://hackmd.io/@pipibear/Sy1E6rEV0) - [2.3 Probably Approximately Correct Learning](https://hackmd.io/@pipibear/SyFf18ENC) - [2.4 Noise](https://hackmd.io/@pipibear/HJUFk84NR) - [2.5 Learning Multiple Classes](https://hackmd.io/@pipibear/BkLSgLE40) - [2.6 Regression](https://hackmd.io/@pipibear/rJhgG8NNA) - [2.7 Model Selection and Generalization](https://hackmd.io/@pipibear/S14QwU4VC) - [2.8 Dimensions of a Supervised Machine Learning Algorithm](https://hackmd.io/@pipibear/SyV3cUVER) - [2.9 Notes](https://hackmd.io/@pipibear/ryVMlD4ER) --- Ch3: Bayesian Decision Theory --- - [3.1 Introduction](https://hackmd.io/@pipibear/Bk7k4zV4R) - [3.2 Classification](https://hackmd.io/@pipibear/SJMJ7ZVER) - [3.3 Losses and Risks](https://hackmd.io/@pipibear/H1Mx7f4NC) - [3.4 Discriminant Functions](https://hackmd.io/@pipibear/B1pu4naNR) - [3.5 Association Rules](https://hackmd.io/@pipibear/SkGwYTREA) - [3.6 Notes](https://hackmd.io/@pipibear/Sylv1eer0) --- Ch4: Parametric Methods --- - [4.1 Introduction](https://hackmd.io/@pipibear/S1oHBgxSA) - [4.2 Maximum Likelihood Estimation](https://hackmd.io/@pipibear/rk9DvfgSC) - [4.3 Evaluating an Estimator: Bias and Variance](https://hackmd.io/@pipibear/BkS_CMuS0) - [4.4 The Bayes' Estimator](https://hackmd.io/@pipibear/rJh3_TsLA) - [4.5 Parametric Classification](https://hackmd.io/@pipibear/BJQLCMAvA) - [4.6 Regression](https://hackmd.io/@pipibear/HyGiwbGO0) - [4.7 Tuning Model Complexity: Bias / Variance Dilemma](https://hackmd.io/@pipibear/Sy7Fs2VOA) - [4.8 Model Selection Procedures](https://hackmd.io/@pipibear/BJ5s6ZIO0) - [補充：Maximum Likelihood Estimation](https://hackmd.io/@pipibear/SJtqpvSBR) - [補充：Bayesian Estimation](https://hackmd.io/@pipibear/Sy4XW138A) - [補充：More Bayesian Concepts ](https://hackmd.io/@pipibear/HJKNTZZDA) - [補充：large coefficients for high-order polynomials](https://hackmd.io/@pipibear/BkyBvBjOR) --- Ch5: Multivariate Methods --- - [5.1 Multivariate Data](https://hackmd.io/@pipibear/S1SaAiPuR) - [5.2 Parameter Estimation](https://hackmd.io/@pipibear/H1TRwnw_C) - [5.3 Estimation of Missing Values](https://hackmd.io/@pipibear/S1zKAoj_C) - [5.4 Multivariate Normal Distribution](https://hackmd.io/@pipibear/SkgL8C3OA) - [5.5 Multivariate Classification](https://hackmd.io/@pipibear/ByBQVbqKR) - [5.6 Tuning Complexity](https://hackmd.io/@pipibear/BJPGvnx5R) - [5.7 Discrete Features](https://hackmd.io/@pipibear/BkuoayG90) - [5.8 Multivariate Regression](https://hackmd.io/@pipibear/SklL4EmcR) - [補充：Exploratory Data Analysis](https://hackmd.io/@pipibear/rkrGpFxF0) - [補充：multivariate outliers](https://hackmd.io/@pipibear/rkalaWUtR) - [補充：Basic Multivariate Normal Theory](https://hackmd.io/@pipibear/HJlnB8wt0) --- Ch6: Dimensionality Reduction --- - [6.1 Introduction](https://hackmd.io/@pipibear/H1z-0mPqR) - [6.2 Subset Selection](https://hackmd.io/@pipibear/r1VW1Id50) - [6.3 Principal Component Analysis](https://hackmd.io/@pipibear/BJOHi5YqC) - [6.4 Feature Embedding]() - [6.5 Factor Analysis]() --- Ch11: Multilayer Perceptrons --- - [11.1 Introduction](https://hackmd.io/@pipibear/HJOKQbNNC) - [11.2 The Perceptron](https://hackmd.io/@pipibear/rJ1Uu_E4R) --- Appendix A: Probability --- - [A.1 Elements of Probability](https://hackmd.io/@pipibear/BJFRH-EEC) - [A.1.1 Axioms of Probability](https://hackmd.io/@pipibear/H16ShcBEC) - [A.1.2 Conditional Probability](https://hackmd.io/@pipibear/Byyhh9BN0) - [A.2 Random Variables](https://hackmd.io/@pipibear/r1S3_f4N0) - [A.2.1 Probability Distribution and Density Functions](https://hackmd.io/@pipibear/HkSF_cS40) - [A.2.2 Joint Distribution and Density Functions](https://hackmd.io/@pipibear/H1JnK9HEC) - [A.2.3 Conditional Distributions](https://hackmd.io/@pipibear/B1HZ5cBNC) - [A.2.4 Bayes' Rule](https://hackmd.io/@pipibear/BJh859SE0) - [A.2.5 Expectation](https://hackmd.io/@pipibear/SJJT95r4R) - [A.2.6 Variance](https://hackmd.io/@pipibear/HyvHi9rEA) - [A.2.7 Weak Law of Large Numbers](https://hackmd.io/@pipibear/BkLfyTHNC) - [補充：sample](https://hackmd.io/@pipibear/Hyn0QQ_H0) - [補充：Bessel's correction](https://hackmd.io/@pipibear/HyIO-uWUA) - [補充： Joint distribution functions](https://hackmd.io/@pipibear/rkrvIg2I0) - [補充：Conditional Distributions](https://hackmd.io/@pipibear/HJB-y57wR) - [A.3 Special Random Variables](https://hackmd.io/@pipibear/BJFj-iq40) - [A.3.1 Bernoulli Distribution](https://hackmd.io/@pipibear/rkGGbs9VA) - [A.3.2 Binomial Distribution](https://hackmd.io/@pipibear/SkWtX39NR) - [A.3.3 Multinomial Distribution](https://hackmd.io/@pipibear/rkFapp94R) - [A.3.4 Uniform Distribution](https://hackmd.io/@pipibear/Hk3uPRcV0) - [A.3.5 Normal(Gaussian) Distribution](https://hackmd.io/@pipibear/rkq0ZkoEC) - [A.3.6 Chi-Square Distribution](https://hackmd.io/@pipibear/HJx5_jz80) - [A.3.7 t distribution](https://hackmd.io/@pipibear/HkU6rYqLC) - [補充：CLT]() - [補充：moment generating function (mgf)](https://hackmd.io/@pipibear/HyWZSx2NR) - [補充：mgf technique](https://hackmd.io/@pipibear/Bk1JuX2VA) - [補充：joint moment generating functions](https://hackmd.io/@pipibear/Hk7Uz2VF0) - [補充：random functions associated with normal distributions](https://hackmd.io/@pipibear/rkYLmUhVR) - [補充：Poisson Distribution](https://hackmd.io/@pipibear/SkrUqjGIA) - [補充：Exponential Distribution](https://hackmd.io/@pipibear/ByC1pvIU0) - [補充：Gamma Distribution](https://hackmd.io/@pipibear/r1agDjvUC) - [補充：Chi-Square Distribution](https://hackmd.io/@pipibear/SJYdTbdIC)
{"title":"ML 筆記","description":"Textbook: Ethem Alpaydin-Introduction to Machine Learning-The MIT Press (2014)","contributors":"[{\"id\":\"40e32ca8-e8f2-4a47-a75e-5b1f2571091a\",\"add\":58139,\"del\":51951}]"}
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