Explores the exponential of a matrix, properties, Frobenius norm, nilpotent matrices, commutativity, and system solutions.
Explores diverse regularization approaches, including the L0 quasi-norm and the Lasso method, discussing variable selection and efficient algorithms for optimization.
Covers model selection, emphasizing the tradeoff between complexity and error in data analysis.
Covers PhD programs at EPFL, including selection procedures, research areas, and support for students.
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
