update week 34

doc/src/week34/week34.do.txt

@@ -28,7 +28,6 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
   * August 19: Lecture: Presentation of course, Linear regression, examples and theory 
   * August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
   * August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
-
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@@ -38,7 +37,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
 ===== Lectures and ComputerLab =====
 
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-  * Mondays: regular lectures/active lerning sessions (10.15am-12pm) 
+  * Mondays: regular lectures/active learning sessions (10.15am-12pm) 
   * The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.
   * Weekly reading assignments and videos needed to solve projects and exercises.
   * Weekly exercises. You can hand in exercises if you want and get an extra score, see below.
@@ -123,25 +122,37 @@ In addition you can get an extra 10% score for weekly assignments (10 in total a
 
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 The lecture notes are collected as a jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html".
+The lecture notes can also be retrieved as a standard PDF file at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/MLbook.pdf".
+
+In addition to the lecture notes, we recommend the books of Rasckha et
+al and Goodfellow et al. We will follow these texts closely and the
+weekly reading assignments refer to these texts. The text by Hastie et
+al is also widely used in the Machine Learning community. See next slide for link to textbooks.
 
-In addition to the lecture notes, we recommend the books of Rasckha  et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. 
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-===== Textbooks =====
+===== Main textbooks =====
 
-!bblock Main texts
+!bblock 
 * Goodfellow, Bengio, and Courville (GBC), Deep Learning URL:"https://www.deeplearningbook.org/"
-* Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili,  Machine Learning with PyTorch and Scikit-Learn at URL:"https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312", see also URL:"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
+* Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili (RLM),  Machine Learning with PyTorch and Scikit-Learn at URL:"https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312", see also URL:"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
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+The weekly reading suggestions are all from these two texts. The text by GBC can be accessed chapter by chapter from the abovementioned URL.
+Each chapter of RLM gives access to the pertinent notebooks. These notebooks are highly recommended.
+
+
+!split
+===== Other popular texts =====
+
 !bblock Other texts
 * Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
 * "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570".
 * "Aurelien Geron (AG), Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/". This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
 * "Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction":"https://probml.github.io/pml-book/book1.html"
-* David Foster, Generative Deep Learning, URL:"https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/"
-* Babcock and Gavras, Generative AI with Python and TensorFlow, URL:"https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2"
+* David Foster (DF), Generative Deep Learning, URL:"https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/"
+* Babcock and Gavras (BG), Generative AI with Python and TensorFlow, URL:"https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2"
 
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