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uttgeorge/Machine-Learning-Models

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Machine Learning

This is the combination of some algorithms and codes from scratch.

Please use Chrome with MathJax plugin.

Email: jin.qi1@northeastern.edu

Topics Introductions Codes
1. Math 1. Bayesian Estimation, MLE, MAP
2. Exponential Family Distribution
 3. Quadratic Form & Quadratic Matrix Defferentiating
 4. Jacobian & Hessian Matrix
 5. Gradient Descent
 6. Newton's Method & Quasi-Newton
 7. EM
2. Evaluation Metrics 1. Classification 中文
2. Regression
3. Perceptron Perceptron Perceptron Code
4. Linear Regression(Not finished)
5. KNN KNN KNN Classifier: mnist handwriting recognition
6. Decision Tree Decision Tree: Feature Selection, Build Tree, Pruning 1. Decision Tree Classifier
2. Decision Tree Regressor
7. Logistic Regression Logistic Regression Logistic Regression Classifier
8. Naive Bayes Naive Bayes Intro Naive Bayes Classifier: GaussianNB, MultinomialNB, BernoulliNB
9. Bagging Random Forest
10. Boosting 1. Adaboost Classifier
2. GBDT
3. XgBoost(Not Finished) 		Adaboost Classifier Code
11. SVM SVM: Duality, KKT, Hard Margin, Soft Margin, SMO Linear SVM Code
12. Kernel Methods Kernels (Not Finished) Kernel SVM (Not Finished)
13. Dimensionality Reduction 1. PCA, SVD, PCoA, PPCA
 2. Linear/Fisher Discriminant Analysis 		1. PCA
2. SVD
14. L1, L2 Regularization L1, L2 Regularization
15. HMM HMM
16. CRF CRF
17. NLP 1. Word Embedding
2. Language model, RNNs, GRU & LSTM

References

  1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An introduction to statistical learning: with applications in R. New York: Springer.
  2. Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer New York, 2016.
  3. StatQuest with Josh Starmer. https://statquest.org
  4. Jie Zhou: https://github.com/shuhuai007/Machine-Learning-Session

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