Update README.md

Author: mhjensen

README.md

@@ -83,28 +83,24 @@ paths:
 - Mathematics of convolutional neural networks
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week4/ipynb/week4.ipynb
 - Recommended reading Goodfellow et al chapter 9. Raschka et al chapter 13
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesFebruary6.pdf
 
 
 ## February 17-21
 - Mathematics of  CNNs and discussion of codes
 - Recurrent neural networks (RNNs)
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesFebruary13.pdf
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week5/ipynb/week5.ipynb
 - Recommended reading Goodfellow et al chapter 9. Raschka et al chapter 13
 
 ## February 24-28
 - Mathematics of recurrent neural networks
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week6/ipynb/week6.ipynb
 - Recommended reading Goodfellow et al chapters 9 and 10 and Raschka et al chapters 14 and 15
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesFebruary20.pdf
 
 ## March 3-7
 - Recurrent neural networks and codes
 - Long-Short-Term memory and applications to differential equations
 - Graph neural network (GNN)s
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week7/ipynb/week7.ipynb
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesFebruary27.pdf
 - Recommended reading Goodfellow et al chapters 10 and Raschka et al chapter 15 and 18
 
 
@@ -113,13 +109,11 @@ paths:
 - Autoencoders and PCA
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week8/ipynb/week8.ipynb
 - Recommended reading Goodfellow et al chapter 14 for Autoenconders and Rashcka et al chapter 18
-- Whiteboard notes https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesMarch5.pdf
 
 ## March 17-21: Autoencoders
 - Autoencoders and links with Principal Component Analysis. Discussion of AE implementations
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week9/ipynb/week9.ipynb  
 - Reading recommendation: Goodfellow et al chapter 14
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesMarch12.pdf
 
 
 ## March 24-28: Generative models
@@ -128,15 +122,13 @@ paths:
 - Boltzmann machines
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week10/ipynb/week10.ipynb  
 - Reading recommendation: Goodfellow et al chapters 16-18
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesMarch19.pdf
 
 ## March 31-April 4: Deep generative models, Boltzmann machines
 - Restricted Boltzmann machines
 - Reminder on Markov Chain Monte Carlo and Gibbs sampling
 - Discussions of various Boltzmann machines
 - Reading recommendation: Goodfellow et al chapters 16, 17 and 18
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week11/ipynb/week11.ipynb  
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesApril2.pdf
 
 
 ## April 7-11: Deep generative models
@@ -145,8 +137,8 @@ paths:
 - Generative Adversarial Networks (GANs)
 - Reading recommendation: Goodfellow et al chapters 18.1-18.2,  20.1-20-7; To create Boltzmann machine using Keras, see Babcock and Bali chapter 4
 - See also Foster, chapter 7 on energy-based models
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesApril10.pdf
 
+## April 14-18: Public holiday, no lectures
 
 ## April 21-25: Deep generative models
 
@@ -155,23 +147,23 @@ paths:
 - Reading recommendation: Goodfellow et al chapter 20.10-20.14
 - See also Foster, chapter 7 on energy-based models
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week13/ipynb/week13.ipynb  
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesApril24.pdf
+
+## April 28 - May 2:  May 1 is a public holiday, no lectures: 
+
 
 ## May 5-9: Deep generative models
 - Variational Autoencoders
 - Diffusion models
 - Reading recommendation: An Introduction to Variational Autoencoders, by Kingma and Welling, see https://arxiv.org/abs/1906.02691 
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week14/ipynb/week14.ipynb  
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesMay8.pdf
 
 ## May 12-16: Deep generative models
 - Summarizing discussion of VAEs
 - Diffusion models
 - Summary of course and discussion of projects
 - Slides at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week15/ipynb/week15.ipynb  
-- Whiteboard notes at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/HandwrittenNotes/2025/NotesMay15.pdf
 
-## May 19-23: Kab only and discussion of projects
+## May 19-23:  Only and discussion of projects
 
 ## Recommended textbooks: