Survey Papers / Repos

• Top 10 algorithms in data mining. [ICDM'06]​

• ​josephmisiti/awesome-machine-learning​

Resources

• ​Coursera Machine Learning by Andrew Ng

Tasks

Supervised

• Linear Regression

• Logistic Regression

• Naive Bayes

• Support Vector Machine (SVM)

  • Training process: Lagrange -> Dual Problem -> SMO

  $\min \frac{1}{2} ||w||^2 \\ \text{s.t.}~y^{(i)}(w^{T}x^{(i)}+b) \geq 1, i=1,...,m$

• K Nearest Neighbor (kNN)

• Expectation-Maximization (EM)

• Linear Discrimant Analysis (LDA)

• Decision Tree

• Random Forest

• Gradient Boosting Tree (GBDT)

Semi-supervised

Weakly-supervised

Unsupervised

• Clustering

  • K-means

  • Mean-shift

  • DBSCAN

• Principal Component Analysis (PCA)

• Latent Dirichlet allocation (LDA) Topic Modeling

Others

Ensemble

• K-Fold Cross Validation

• Bagging

• Boosting

Metrics

• Precision and Recall

  • ​$\text{Precision} = \frac{\text{TP}}{\text{TP} +\text{FP}}$​

  • ​$\text{Recall} = \frac{\text{TP}}{\text{TP}+\text{FN}}$​

• F1 Score

  • ​$\text{F1 score} = 2 \cdot\frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} +\text{Recall}}$​

• Receiver Operating Characteristic (ROC)

  • ​$\text{TPR} = \frac{\text{TP}}{\text{TP}+\text{FN}}$​

  • ​$\text{FPR} = \frac{\text{FP}}{\text{FP}+\text{TN}}$​

• Area Under ROC (AUC)

• Confusion Matrix

Reference

• SVM: https://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.html​

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