Explore the evolution from classical statistics to modern machine learning in this 50-minute lecture by Mikhail Belkin from The Ohio State University. Delve into supervised ML, generalization bounds, and the classical U-shaped generalization curve. Examine the concept of interpolation in deep learning, addressing whether it leads to overfitting and its effectiveness even with noisy data. Investigate the "double descent" risk curve, its mechanisms, and implications for linear regression. Analyze the landscape of generalization, optimization under interpolation, and the power of interpolation in modern ML techniques. Gain insights into fast and effective kernel machines inspired by deep learning, and understand the key points in the transition from classical statistical approaches to contemporary machine learning methodologies.