diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index b5bce5f00..76f00da39 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -214,6 +214,11 @@ We plan to cover the following topics:
   * Decisions trees, Random Forests, Bagging and Boosting methods;
   * Support vector machines (only survey);
   * Unsupervised learning and  dimensionality reduction, from PCA to clustering;  
+
+
+!split
+===== Deep learning methods =====
+
 * Deep learning  
   * Neural networks and deep learning;
   * Convolutional neural networks;
@@ -262,22 +267,6 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
 ===== Learning outcomes =====
 
 !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,
-normally three, you will be exposed to fundamental research problems
-in these fields, with the aim to reproduce state of the art scientific
-results. Both supervised and unsupervised methods will be covered. The
-emphasis is on a frequentist approach, although we will try to link it
-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
-
 * 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;