t.mcandrew

Latest

• (Class 24) Ensembles: Stacking
• (Class 23) Ensembles: Bagging
• (Class 22) Tree-based regression
• (Class 21) Ridge Regression and LASSO
• (Class 20) Collaborative filtering and KNN
• (Class 19) Logistic Regression for a Binomial R.V.
• (Class 18) KNN for Classification
• (Class 17) Logistic Regression Lab
• (Class 16) Bernoulli, Binomial, and MLE
• (Class 15) Bernoulli, Binomial, and MLE
• (Class 14) Interpreting coefficients and measures of accuracy
• (Class 13) Classification
• (Class 11) Transform lab
• (Class 12) KNN regression
• (Class 10) Transformations
• (Class 09) Bias-variance tradeoff
• (Class 08) Cross-validation Lab
• (Class 07) Orthogonal projections as a method of optimization.
• (Class 06) Testing, training, and validation data, and the Bias-variance tradeoff.
• (Class 05) SSE optimization.
• (Class 04) Lab on fitting simple, multiple, and polynomial models to Boston housing data.
• (Class 03) Polynomial regression, optimizing parameters via SSE
• (Class 02) Polynomial regression, testing and training
• (Class 01) Technical setup and Matrix notation related to regression