Pinned Repositories
BIDS10-ClassificationRegression4
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.
BIDS11-AssignmentData
All materials for the assignment can be found here
BIDS2-DimensionReduction1
This session is focussed on what dimension reduction is, what it can be used for and revolves around Principal Component Analysis (PCA).
BIDS3-DimensionReduction2
This session explores two further methods that can be used for dimension reduction: Multi-Dimensional Scaling (MDS) and (optional) Non-negative Matrix Factorization (NMF).
BIDS4-DimensionReduction3
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).
BIDS5-Clustering1
This session introduces clustering and deals with three basic methods still widely used: k-Nearest Neighbours (kNN), k-Means and hierarchical clustering.
BIDS6-Clustering2
This session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
BIDS7-ClassificationRegression1
This session introduces supervised learning and focusses on Partial Least Squares (PLS) and penalised (lasso, ridge, elastic net) regression methods.
BIDS8-ClassificationRegression2
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).
Data
Datasets for module tutorials.
ICL-BMB-BiDS's Repositories
ICL-BMB-BiDS/BIDS2-DimensionReduction1
This session is focussed on what dimension reduction is, what it can be used for and revolves around Principal Component Analysis (PCA).
ICL-BMB-BiDS/BIDS3-DimensionReduction2
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/Data
Datasets for module tutorials.
ICL-BMB-BiDS/BIDS10-ClassificationRegression4
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/BIDS11-AssignmentData
All materials for the assignment can be found here
ICL-BMB-BiDS/BIDS4-DimensionReduction3
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/BIDS5-Clustering1
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/BIDS6-Clustering2
This session deals with Gaussian Mixture Models (GMMs) and density-based clustering methods.
ICL-BMB-BiDS/BIDS7-ClassificationRegression1
This session introduces supervised learning and focusses on Partial Least Squares (PLS) and penalised (lasso, ridge, elastic net) regression methods.
ICL-BMB-BiDS/BIDS8-ClassificationRegression2
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/BIDS9-ClassificationRegression3
This session explores ensemble methods Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs).