Module III: ML and Deep Learning

flowchart LR

  DSc --> Unsup(Dim. Reduction) --> Clust(Clustering) --> MM(Mixtures) --> HDB(HDBScan)
  Unsup --> PCA --> CA --> UMAP

  DSc --> Cl(Classification) --> T(Tree-based Models) --> BG(Bagging) --> XG(Boosting)

  Cl --> Im(Imbalance) --> F(Fraud Detection)

  DSc(Decisions, Scale) --> Text[/NLP/] --> EM[Embeddings] --> Attn[Attention] --> ABSA[ABSA]
  DSc --> RS[/RecSys/] --> Mtr[Metrics] --> FM[Factorization Mach.] --> HM[Hybrid Models]
  DSc --> CV(DL: Vision) --> Conv[CNNs] --> AK[Approx. kNN]

  Conv --> HM

  DSc --> TS(Time Series) --> MTS[Metrics] --> XGB[ML Approaches] --> DL[DL Approaches]
  EM --> HM

Figure 1: In this ML/DL module, we focus on practical, challenging use-cases and reliable, workhorse methods – while keeping in mind the particularities of the domain and applications.