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    11 repositories

    • BIDS11-AssignmentData

      Public
      All materials for the assignment can be found here
      0•0•0•0•Updated Aug 1, 2024Aug 1, 2024

    • BIDS8-ClassificationRegression2

      Public
      This session revolves around what kernels are, why they are used in supervised learning, and how they are used with Support Vector Machines (SVMs) for classification (SVC) and regression (SVR).
      Jupyter Notebook
      •0•0•0•0•Updated May 21, 2024May 21, 2024

    • BIDS10-ClassificationRegression4

      Public
      This session is dedicated to an introduction of (artificial) neural networks and discusses a basic network architecture for classification, the (multilayer) feedforward neural network (FNN), and an unsupervised network, the autoencoder (AE), which can be used in a classification setting.
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS9-ClassificationRegression3

      Public
      This session explores ensemble methods Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs).
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS7-ClassificationRegression1

      Public
      This session introduces supervised learning and focusses on Partial Least Squares (PLS) and penalised (lasso, ridge, elastic net) regression methods.
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS6-Clustering2

      Public
      This session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS5-Clustering1

      Public
      This session introduces clustering and deals with three basic methods still widely used: k-Nearest Neighbours (kNN), k-Means and hierarchical clustering.
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS4-DimensionReduction3

      Public
      This session is dedicated to two recent methods for dimension reduction: t-distributed Stochastic Neighbour Embeddings (t-SNE) and Uniform Manifold Approximation and Projection (UMAP).
      Jupyter Notebook
      •0•0•0•0•Updated May 12, 2024May 12, 2024

    • BIDS3-DimensionReduction2

      Public
      This session explores two further methods that can be used for dimension reduction: Multi-Dimensional Scaling (MDS) and (optional) Non-negative Matrix Factorization (NMF).
      Jupyter Notebook
      •0•1•0•0•Updated May 12, 2024May 12, 2024

    • BIDS2-DimensionReduction1

      Public
      This session is focussed on what dimension reduction is, what it can be used for and revolves around Principal Component Analysis (PCA).
      Jupyter Notebook
      •0•1•0•0•Updated May 12, 2024May 12, 2024

    • Data

      Public
      Datasets for module tutorials.
      0•1•0•0•Updated May 26, 2022May 26, 2022