Umberto Michelucci studied physics and mathematics. He is a proven expert in numerical simulation, statistics, data science and machine learning. He has continuously extended his expertise in post-graduate courses and research projects over the years. In addition to several years of research experience at George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in data warehousing, data science, and machine learning. In 2014, he completed a “Postgraduate Certificate in Professional Studies in Education” in England to expand his knowledge in teaching and pedagogy.
He is the author of “Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks” (www.applieddeeplearningbook.com), published by Springer in 2018. His second book on “Convolutional and Recurrent Neural Networks Theory and Applications” came out in 2019. He is very active in Artificial Intelligence research. He regularly publishes his research results in leading journals and he regularly gives talks at international conferences.
He is a lecturer at ZHAW University of Applied Sciences for Deep Learning.
He is also the founder of TOELT GmbH, a company with the goal of developing new and modern teaching methods for AI, and making AI technologies accessible to everyone.