From 44e2824405aa8aa54af4c07f7669e65307e30cb8 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 1 Sep 2021 22:51:17 +0200 Subject: [PATCH] Update week35.do.txt --- doc/src/week35/week35.do.txt | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/doc/src/week35/week35.do.txt b/doc/src/week35/week35.do.txt index de59a0647..69b4b2b0f 100644 --- a/doc/src/week35/week35.do.txt +++ b/doc/src/week35/week35.do.txt @@ -13,12 +13,17 @@ DATE: today !split ===== Thursday August 27 ===== -"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureAug27.mp4?vrtx=view-as-webpage". - +The main topics on Thursday are: +o Repetition from last week on linear regression +o Discussion of how to prepare data and examples of applications of linear regression +o Mathematical interpretations of Linear Regression +o Start discussing Ridge regression and Singular Value Decomposition !split ===== Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week ===== +We need first a reminder from last week about linear regression. + Fitting a continuous function with linear parameterization in terms of the parameters $\bm{\beta}$. * Method of choice for fitting a continuous function! * Gives an excellent introduction to central Machine Learning features with _understandable pedagogical_ links to other methods like _Neural Networks_, _Support Vector Machines_ etc