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1. Data Data Public

   Datasets for module tutorials.

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2. BIDS2-DimensionReduction1 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 2

3. BIDS3-DimensionReduction2 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 2

4. BIDS11-AssignmentData BIDS11-AssignmentData Public

   All materials for the assignment can be found here

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5. BIDS4-DimensionReduction3 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 1

6. BIDS5-Clustering1 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 1