Embark on a fast-paced journey through machine learning fundamentals in this 1-hour 8-minute tutorial presented by Stefan Chmiela from Technische Universität Berlin at IPAM's Advancing Quantum Mechanics with Mathematics and Statistics Tutorials. Dive into key concepts such as inductive bias, underfitting and overfitting, optimal model complexity, and regularization techniques. Explore linear and nonlinear regression, kernel methods, and matrix factorization. Gain insights into data limitations, cross-validation, and the kernel trick. Discover how these principles apply to energy contributions and iterative optimization techniques, concluding with a discussion on the tradeoffs involved in nonlinear approaches.