I. Machine Learning
I. Machine Learning
Part I explains almost everything about machine learning "except for learners". Don't panic, the remaining lectures are all about learners.
Part I explains almost everything about machine learning "except for learners". Don't panic, the remaining lectures are all about learners.
Machine Learning?
Supervised vs. Unsupervised
Regression vs. Classification
Training vs. Test Data sets
What Do We Do with Cross-Validation?
Cross-Validation in Time Series
Mean Squared Error(MSE)
Bias-Variance Decomposition of MSE
Meaning of the Decomposition in Classification Models
Confusion Matrix in Classification
Hello, again, econometrics !! Frequently, no hyper parameters to tune in Part II. Oops, we have a tunable parameter K in KNN.
Estimation of Linear Regression
Tests in Linear Regression
Appendix. Generalized Least Squares
A1. Generalized Least Squares(GLS) [PDF]
A2. Restricted Least Squares [PDF]
A3. Instrumental Variables [PDF]
A4. Seemingly Unrelated Regression(SUR) [PDF]
A5. Vector Autoregression(VAR) [PDF]
A6. Panel Regression 1: No Fixed Effects [PDF]
A7. Panel Regression 2: Fixed Effects [PDF]
Representations of Logistic Regression
Estimation
Performance and Tests
KNN Regression
KNN in Classification