From a8afe6b0209a158b7e4a6d93d0496696269528c3 Mon Sep 17 00:00:00 2001
From: Jan Benda <jan.benda@uni-tuebingen.de>
Date: Tue, 10 Dec 2019 10:00:20 +0100
Subject: [PATCH] updated gradient descend exercise

---
 regression/exercises/exercises01.tex | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/regression/exercises/exercises01.tex b/regression/exercises/exercises01.tex
index a58e3da..6d9e335 100644
--- a/regression/exercises/exercises01.tex
+++ b/regression/exercises/exercises01.tex
@@ -20,7 +20,7 @@
 \else
 \newcommand{\stitle}{}
 \fi
-\header{{\bfseries\large Exercise 10\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large December 17th, 2018}}
+\header{{\bfseries\large Exercise 10\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large December 16th, 2019}}
 \firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
   jan.grewe@uni-tuebingen.de}
 \runningfooter{}{\thepage}{}
@@ -121,7 +121,8 @@
 
     \part Use the functions \code{polyfit()} and \code{lsqcurvefit()}
     provided by matlab to find the slope and intercept of a straight
-    line that fits the data.
+    line that fits the data. Compare the resulting fit parameters of
+    those functions with the ones of your gradient descent algorithm.
     \begin{solution}
       \lstinputlisting{../code/linefit.m}
     \end{solution}