| Week |
Date |
Topic(s) |
Instructions |
| 39 |
Sep 22 |
The statistical language R: Basic commands, data types, arrays and matrices, functions, probability distributions, statistical models, graphics, importing and exporting data |
Tasks /
Files
|
| |
Sep 23 |
Regression analysis: Orthogonal transformations.
Least squares: normal equations vs QR decomposition. Polynomial
regression and the correlation matrix. Illustration using
scatter plots. Single and multiple outputs
|
Tasks /
Files
|
|
Sep 24 |
Regression analysis, cont.: Numerical rank deficiency, collinearity. SVD. Subset selection. Ridge regression. Principal components regression.
Application in pattern recognition: classification of handwritten digits (regression) |
Tasks /
Files
|
| |
Sep 25 |
Sparse matrices in statistics: Regression with
sparse matrices. Text mining |
Tasks /
Files
|
| |
Sep 26 |
Classification using an SVD basis method |
Tasks(a) /
Tasks(b) /
Files
|
| 41 |
Oct 6 |
Structured matrices, Simulating data with certain
invariance properties.
|
Tasks /
Files
|
| |
Oct 7 |
Total Least Squares: Test problems, numerical algorithms,
computational complexity, comparison with classical LS |
Tasks /
Files
|
| |
Oct 8 |
Partial Least Squares |
Tasks /
Files
|
| |
Oct 9 |
The time is intended to be used for discussions and completing tasks from the first three labs from Week 41. |
Tasks /
Files
|