From f707410252d9ca5a63aecaf51e81b94f7e17a650 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 16 Sep 2020 13:54:27 +0200 Subject: [PATCH] changes to week37 --- doc/src/week37/week37.do.txt | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/doc/src/week37/week37.do.txt b/doc/src/week37/week37.do.txt index aa540f7dd..ef0b58b91 100644 --- a/doc/src/week37/week37.do.txt +++ b/doc/src/week37/week37.do.txt @@ -8,9 +8,17 @@ DATE: today * Thursday September 10: Motivation for shrinkage methods and the Singular Value Decompostion theorem * Friday September 11: Ridge and Lasso regression + +!split +===== Thursday September 10 ===== + +"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage" and "handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember10.pdf" + +More material will be added here, see handwritten notes also. !split ===== A Bayesian approach to develop intuition about skrinkage methods ===== +See Handwritten notes !split ===== The singular value decomposition ===== @@ -332,6 +340,15 @@ It is indeed the economy-sized SVD, note the summation runs up tp $$p$$ only and Here we have that $${\bf X} = {\bf U}{\bf \Sigma}{\bf V}^T$$, with $$\Sigma$$ being an $$ n\times p$$ matrix and $${\bf V}$$ being a $$ p\times p$$ matrix. We also have assumed here that $$ n > p$$.  + +!split +===== Friday September 12 ===== + +"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage" and "handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf" + +More material will be added here, see handwritten notes also. + + !split ===== Ridge and LASSO Regression =====