Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Explores the construction of measures, emphasizing positive functionals and their connection to the Riesz-Kakutani Theorem.
Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.
