开源软件名称(OpenSource Name):mxc19912008/Andrew-Ng-Machine-Learning-Notes
开源软件地址(OpenSource Url):https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes
开源编程语言(OpenSource Language):
开源软件介绍(OpenSource Introduction):Andrew-Ng-Machine-Learning-Notes
The notes of Andrew Ng Machine Learning in Stanford University
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1. Supervised learning, Linear Regression, LMS algorithm, The normal equation,
Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression
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2. Generative Learning algorithms, Gaussian discriminant analysis, Naive Bayes, Laplace smoothing, Multinomial event model
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3. SVM, Lagrange duality, Kernel, SMO
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4. Bias-Variance trade-off, Learning Theory
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5. Cross-validation, Feature Selection, Bayesian statistics and regularization
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6. Online Learning, Online Learning with Perceptron
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7a. K-Means
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7b. EM Algorithm, GMM
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8. EM Algorithm in detail
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9. Factor Analysis, EM for Factor Analysis
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10. PCA
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11. Independent Component Analysis(ICA)
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12. Reinforcement Learning
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Tree based ML algorithms
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Deep Learning Notes
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