Explores hierarchical control concepts in robotics, including switching, prioritizing, and information chains, to achieve complex robot behaviors.
Explores controlling behavior in animals and robots, covering historical perspectives, neuron activation, Drosophila model, advanced techniques, and mini-project organization.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Covers linear regression, correlation analysis, and logistic regression fundamentals, emphasizing the distinction between correlation and causation.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
