yuehhua/machine-learning.jl

Machine Learning

Supervised Learning

Regression

1. Linear regression
2. Poisson regression
3. LASSO regression
4. Ridge regression
5. Polynomial regression

Classification

7. Logistic regression
8. Naive Bayes
9. Decision tree
10. Random forest
11. Adaboost
12. SVM
13. kNN

Clustering

13. k-means
14. k-medoids
15. Heirarchical clustering
16. MCL
17. DBSCAN
18. Affinity propagation
19. Fuzzy c-means

Dimensional reduction

20. PCA
21. LDA
22. MDS
23. t-SNE
24. Non-negative matrix factorization
25. Probablistic PCA
26. Kernel PCA
27. ICA
28. UMAP
29. Diffusion map

Validation

30. K-fold cross validation

Packages

Distributions

• Distributions.jl

Regression

• Lasso.jl
  • Ridge regression
  • LASSO regression
  • ElasticNet
• LARS.jl
  • Least angle regression
  • L1-regularized linear regression
• Isotonic.jl
  • Linear PAVA (fastest)
  • Pooled PAVA (slower)
  • Active Set (slowest)

Clustering

• Clustering.jl
  • K-means
  • K-medoids
  • Affinity Propagation
  • Density-based spatial clustering of applications with noise (DBSCAN)
  • Markov Clustering Algorithm (MCL)
  • Fuzzy C-Means Clustering
  • Hierarchical Clustering
    • Single Linkage
    • Average Linkage
    • Complete Linkage
    • Ward's Linkage

Dimensional Reduction

• MultivariateStats.jl
  • Data Whitening
  • Principal Components Analysis (PCA)
  • Canonical Correlation Analysis (CCA)
  • Classical Multidimensional Scaling (MDS)
  • Linear Discriminant Analysis (LDA)
  • Multiclass LDA
  • Independent Component Analysis (ICA), FastICA
  • Probabilistic PCA
  • Factor Analysis
  • Kernel PCA
• NMF.jl
  • Lee & Seung's Multiplicative Update (for both MSE & Divergence objectives)
  • (Naive) Projected Alternate Least Squared
  • ALS Projected Gradient Methods
  • Random Initialization
  • NNDSVD Initialization
• TSne.jl
• UMAP.jl

Kernel Density Estimtion

• KernelDensity.jl

Time Series Analysis

• TimeSeries.jl