Feature Extraction & Clustering Methods

This lecture introduces methods for feature extraction, clustering, and classification in the context of a mini-project. It covers topics such as dimensionality reduction, principal component analysis (PCA), wavelet transformation, t-distributed stochastic neighbor embedding (t-SNE), k-means clustering, Gaussian mixture model (GMM), and watershed transform. The instructor discusses the application of these techniques to high-dimensional datasets, neuronal and behavioral data analysis, and the combination of dimensionality reduction with clustering for behavioral analysis. Various classification algorithms like decision trees, random forests, naive Bayes, and support vector machines (SVMs) are also explained, along with performance evaluation metrics and machine learning libraries in Python.