Explore a lecture on guaranteed training methods for neural networks using tensor decomposition techniques. Delve into a novel approach that provides risk bounds for training two-layer neural networks with polynomial sample and computational complexity. Learn how unsupervised learning can enhance supervised tasks through the estimation of probabilistic score functions. Discover insights from Anima Anandkumar, a faculty member at U.C. Irvine's EECS Department, as she discusses her research in large-scale machine learning and high-dimensional statistics. Gain valuable knowledge about overcoming the non-convex optimization challenges in neural network training and avoiding local optima, especially in high-dimensional spaces.