diff --git a/doc/src/week39/week39.do.txt b/doc/src/week39/week39.do.txt
index 7b190e701..3bd2c85f0 100644
--- a/doc/src/week39/week39.do.txt
+++ b/doc/src/week39/week39.do.txt
@@ -1,4 +1,4 @@
-TITLE: Week 39: Optimization and  Gradient Methods
+TITLE: Week 39: Resampling methods and logistic regression
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo
 DATE: Week 39
 
@@ -11,16 +11,8 @@ DATE: Week 39
 ===== Lecture Monday September 22 =====
 
 !bblock Material for the lecture on Monday September 22
-  * Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of gradient methods
-  * Stochastic Gradient descent with examples and automatic differentiation (theme also for next week).
 #  * "Video of lecture":"https://youtu.be/ISGpTC28Vmk"
 #  * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember23.pdf"
-  * Readings and Videos:
-    * These lecture notes
-    * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.
-    * Raschka et al, pages 53-76 on Logistic regression and pages 37-52 on gradient optimization    
-    * "Video on gradient descent":"https://www.youtube.com/watch?v=sDv4f4s2SB8"
-    * "Video on stochastic gradient descent":"https://www.youtube.com/watch?v=vMh0zPT0tLI"
 !eblock
 
 !split