diff --git a/doc/src/week39/week39.do.txt b/doc/src/week39/week39.do.txt
index 2b6439aab..2f3e51097 100644
--- a/doc/src/week39/week39.do.txt
+++ b/doc/src/week39/week39.do.txt
@@ -1,20 +1,20 @@
 TITLE: Week 39: Optimization and  Gradient Methods
-AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University
+AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo
 DATE: Week 39
 
 !split
-===== Plan for week 39, September 23-27, 2024 =====
+===== Plan for week 39, September 22-26, 2025 =====
 
 
 
 !split
-===== Lecture Monday September 23 =====
+===== Lecture Monday September 22 =====
 
-!bblock Material for the lecture on Monday September 23
+!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"
+#  * "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.
@@ -38,7 +38,7 @@ DATE: Week 39
 
 
 !split
-===== Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm =====
+===== Lecture Monday September 22, Optimization, the central part of any Machine Learning algortithm =====
 
 The first few slides here are a repetition from last week.