Explore nonlinear independent component analysis in this seminar on theoretical machine learning presented by Aapo Hyvärinen from the University of Helsinki. Delve into the fundamental differences between ICA and PCA, examine the success of artificial intelligence and deep learning, and understand the importance of unsupervised learning. Discover how ICA performs blind source separation, with examples in brain source separation and image features. Investigate the unsolved problem of nonlinear ICA, including the Darmois construction and how temporal structure aids in nonlinear ICA. Learn about the algorithmic trick of "self-supervised" learning and the theorem on TCL estimates for nonlinear nonstationary ICA. Examine permutation contrastive learning and its demixing capability, as well as extensions of nonlinear ICA on time series. Explore the general framework of Deep Latent Variable Models and how conditioning makes DLVM identifiable. Gain insights into alternative approaches to the DLVM case in this comprehensive seminar on advanced machine learning concepts. Read more