ICL-BMB-BiDS
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BIDS2-DimensionReduction1
BIDS2-DimensionReduction1 PublicThis session is focussed on what dimension reduction is, what it can be used for and revolves around Principal Component Analysis (PCA).
Jupyter Notebook 1
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BIDS3-DimensionReduction2
BIDS3-DimensionReduction2 PublicThis 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 1
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BIDS4-DimensionReduction3
BIDS4-DimensionReduction3 PublicThis 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
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BIDS5-Clustering1
BIDS5-Clustering1 PublicThis session introduces clustering and deals with three basic methods still widely used: k-Nearest Neighbours (kNN), k-Means and hierarchical clustering.
Jupyter Notebook
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BIDS6-Clustering2
BIDS6-Clustering2 PublicThis session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
Jupyter Notebook
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BIDS7-ClassificationRegression1
BIDS7-ClassificationRegression1 PublicThis session introduces supervised learning and focusses on Partial Least Squares (PLS) and penalised (lasso, ridge, elastic net) regression methods.
Jupyter Notebook
Repositories
- 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.
ICL-BMB-BiDS/BIDS10-ClassificationRegression4’s past year of commit activity - BIDS7-ClassificationRegression1 Public
This session introduces supervised learning and focusses on Partial Least Squares (PLS) and penalised (lasso, ridge, elastic net) regression methods.
ICL-BMB-BiDS/BIDS7-ClassificationRegression1’s past year of commit activity - 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.
ICL-BMB-BiDS/BIDS5-Clustering1’s past year of commit activity - 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).
ICL-BMB-BiDS/BIDS4-DimensionReduction3’s past year of commit activity - BIDS6-Clustering2 Public
This session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
ICL-BMB-BiDS/BIDS6-Clustering2’s past year of commit activity - BIDS9-ClassificationRegression3 Public
This session explores ensemble methods Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs).
ICL-BMB-BiDS/BIDS9-ClassificationRegression3’s past year of commit activity - 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).
ICL-BMB-BiDS/BIDS8-ClassificationRegression2’s past year of commit activity - 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).
ICL-BMB-BiDS/BIDS3-DimensionReduction2’s past year of commit activity
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