Data Mining
- introduction: data(different types, pre-processing, visualization)
- Similarities and dissimilarities
- Regression (linear and non-linear)
- Classification (Decision Trees, rule based methods, KNN, Nave Bayes, Neural Networks, SVM, Statistical Methods, K-fold, bootstrap)
- Association Rules
- Clustering(Hard, Soft, Partitioning, Hierarchical)
- Clustering methods(K-means, Fuzzy C-means, Spectral Clustering, )
- Anomaly Detection