Phase 11
Statistics & Statistical Learning
Phase 11 of the Quant Academy curriculum.
11.1Full Lesson
Statistical Inference, Estimation, and the Bias-Variance Decomposition
What an estimator is, why maximum likelihood is the workhorse, and the single decomposition that governs every model you
11.2Full Lesson
Linear Regression: OLS, Geometry, Gauss-Markov, and Inference vs Prediction
The normal equations, least squares as an orthogonal projection, when OLS is best, and the crucial split between explain
11.3Full Lesson
Model Assessment: Cross-Validation and Model Selection
Why training error lies, how cross-validation estimates out-of-sample error honestly, and how AIC/BIC and the one-standa
11.4Full Lesson
Regularization: Ridge and Lasso
Shrinking coefficients on purpose - the closed-form ridge estimator, the sparsity of the lasso, and why bias buys
11.5Full Lesson
Classification: Logistic Regression and LDA
Modeling a probability instead of a mean - the logit link and its MLE, linear discriminant analysis, and how the t
11.6Full Lesson
Trees, Ensembles, Boosting, and Support Vector Machines
Greedy recursive partitions, the variance-cut of bagging and random forests, the bias-cut of boosting, and the max-margi
11.7Full Lesson
Unsupervised Learning, Dimensionality Reduction, and Financial-Data Pitfalls
PCA and clustering as structure-finders - and the research-integrity essentials: data leakage, look-ahead bias, an