: Matrices, vectors, and operations critical for machine learning algorithms.
: Utilizing data collection instruments and analysis techniques. Case Studies
: Vectors, matrices, and their applications in data transformations.
: Differentiation and integration, focusing on optimization techniques (e.g., gradient descent).