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    12 repositories

    • BIDS11-Resit

      Public
      Materials for the resit of BIDS
      0•0•0•0•Updated Jul 23, 2025Jul 23, 2025

    • BIDS11-AssignmentData

      Public
      All materials for the assignment can be found here
      0•0•0•0•Updated May 23, 2025May 23, 2025

    • 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 20, 2025May 20, 2025

    • 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 18, 2025May 18, 2025

    • 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 18, 2025May 18, 2025

    • 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 18, 2025May 18, 2025

    • BIDS6-Clustering2

      Public
      This session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
      Jupyter Notebook
      •0•0•0•0•Updated May 18, 2025May 18, 2025

    • 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 18, 2025May 18, 2025

    • 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 18, 2025May 18, 2025

    • 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 16, 2025May 16, 2025

    • 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 16, 2025May 16, 2025

    • Data

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