Fundamental Mathematical Concepts for Machine Learning in Science

Umberto Michelucci has a PhD in Machine Learning and Physics from the University of Portsmouth. He is the cofounder and Chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. He’s an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His first book, Applied Deep Learning—A Case-Based Approach to Understanding Deep Neural Networks, was published by Apress in 2018. He followed with Convolutional and Recurrent Neural Networks Theory and Applications in 2019. He’s very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences. Umberto studied physics and mathematics. Sharing is caring—for that, he is a lecturer at the ZHAW University of Applied Sciences for deep learning and neural networks theory and applications. He’s also responsible at Helsana Versicherung AG for research and collaborations with universities in the area of AI. He is also a Google Developer Expert in Machine Learning based in Switzerland.

• 1. Introduction.- 2. Calculus and Optimisation for Machine Learning.- 3. Linear Algebra.- 4. Statistics and Probability for Machine Learning.- 5. Sampling Theory (a.k.a. Creating a Dataset Properly).- 6. Model Validation.- 7. Unbalanced Datasets.- 8. Hyperparameter Tuning.- 9. Model Agnostic Feature Importance.