Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.
Explores controlling behavior in animals and robots, covering historical perspectives, neuron activation, Drosophila model, advanced techniques, and mini-project organization.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
