Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.
Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Covers the role of models and data in statistical learning and optimization formulations, with examples of classification, regression, and density estimation problems.
Explores multilinear regression for design optimization and orthogonality, covering teamwork, abstracts, linear and quadratic models, ANOVA, and alias structures.
