This lecture covers the basics of machine learning, including supervised and unsupervised learning, model evaluation, and feature selection. It also delves into advanced topics such as neural networks and deep learning.
Nicolai Cramer was born in Stuttgart, Germany; he studied chemistry at the University of Stuttgart where he graduated in 2003, and earned his PhD in 2005 under the guidance of Professor Sabine Laschat. After a research stage at Osaka University, Japan, he joined the group of Professor Barry M. Trost at Stanford University as a postdoctoral fellow in 2006. From 2007 on, he worked on his habilitation at the ETH Zurich associated to the chair of Professor Erick M. Carreira and recieved the venia legendi in 2010. In 2010, he started as Assistant Professor at the EPF Lausanne and was promoted to Associate Professor in 2013 and to Full Professor in 2015. His main research program encompasses enantioselective metal-catalyzed transformations and their implementation for the synthesis of biologically active molecules.
Author profile (Angew. Chem. Int. Ed.)
CV
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To develop basic understanding of the reactivity of aromatic and heteroaromatic compounds. To develop a knowledge of a class of pericyclic reactions. To apply them in the context of the synthesis.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
