ML Foundations for Quants
The statistical-learning toolkit every quant agent is built on, with time-series validation as the discipline that keeps it honest.
Supervised learning maps features to labels, predicting returns, defaults, or volatility, while unsupervised methods cluster regimes and compress noisy factor spaces. Feature engineering is where domain knowledge enters: lags, rolling statistics, and cross-sectional ranks beat exotic architectures on tabular market data. Validation is the discipline that separates this field, because random k-fold leaks the future, so quants use purged and embargoed cross-validation that respects the temporal ordering of overlapping labels. Finance punishes overfitting harder than almost any other domain because noise dominates signal, backtests multiply researcher degrees of freedom, and live capital is the grader. These foundations precede every neural or agentic method later in the track.