diff --git a/doc/src/week34/beamerweek34.do.txt b/doc/src/week34/beamerweek34.do.txt
index 153e652cd..13e6b3e64 100644
--- a/doc/src/week34/beamerweek34.do.txt
+++ b/doc/src/week34/beamerweek34.do.txt
@@ -128,6 +128,8 @@ In addition to the lecture notes, we recommend the books of Bishop and Goodfello
 
 o Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, URL:"https://www.springer.com/gp/book/9780387310732". 
 
+o "Kevin Murphy, Probabilistic Machine Learning":"https://probml.github.io/pml-book/book1.html"
+
 o Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at URL:"https://www.deeplearningbook.org/". Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
 
 Additional textbooks:
@@ -158,7 +160,6 @@ Python is the recurring programming language.
 
 !bblock
 
-
 This course aims at giving you insights and knowledge about many of
 the central algorithms used in Data Analysis and Machine Learning.
 The course is project based and through various numerical projects,
@@ -170,9 +171,14 @@ with a Bayesian approach as well. You will learn to develop and
 structure large codes for studying different cases where Machine
 Learning is applied to, get acquainted with computing facilities and
 learn to handle large scientific projects. A good scientific and
-ethical conduct is emphasized throughout the course. More
-specifically, after this course you will
+ethical conduct is emphasized throughout the course.
+!eblock
 
+!split
+===== More on learning outcomes =====
+More
+specifically, after this course you will
+!bblock
 * Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
 * Be capable of extending the acquired knowledge to other systems and cases;
 * Have an understanding of central algorithms used in data analysis and machine learning;
@@ -194,6 +200,10 @@ The course has two central parts
 o Statistical analysis and optimization of data
 o Machine learning
 
+
+!split
+===== Detailed description =====
+
 These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
 
 !bblock  Statistical analysis and optimization of data