3. Risk Minimization and Bayes optimal

"This lecture defines supervised learning as a mathematical problem based on training data comprised of input-output pairs. Assuming a stochastic relationship between inputs and outputs, the instructor critiques a worst-case loss analysis, demonstrating that it trivially collapses into a useless constant function. Instead, the lecture transitions to risk minimization by focusing on average-case or expected loss minimization. This objective ensures generalization to unseen examples, introducing key terminology like risk, base optimal, and base risk. Finally, the framework is applied to two specialized domains: regression for continuous vectors using squared loss, and classification for discrete labels using 0-1 loss. References: Study lectures 4,5,6 in https://www.overleaf.com/read/kggbvxh... and references therein; Slides: https://iith-my.sharepoint.com/:p:/g/..."