diff --git a/doc/src/week34/beamerweek34.do.txt b/doc/src/week34/beamerweek34.do.txt
index b13ad0a0a..153e652cd 100644
--- a/doc/src/week34/beamerweek34.do.txt
+++ b/doc/src/week34/beamerweek34.do.txt
@@ -67,7 +67,7 @@ Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2.
 ===== Communication channels =====
 
 * Chat and communications via URL:"canvas.uio.no"
-* _Slack_ channel: machinelearninguio.slack.com
+* _Slack_ channel: machinelearninguio.slack.com, see invitation sent by email
 
 
 !split
@@ -76,9 +76,8 @@ Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2.
 !bblock
   * Three compulsory projects. Electronic reports only using "Canvas":"https://www.uio.no/english/services/it/education/canvas/" to hand in projects and "git":"https://git-scm.com/" as version control software and "GitHub":"https://github.com/" for repository (or "GitLab":"https://about.gitlab.com/") of all your material.
   * Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
-    o For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project.
-    o If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course
-    o Poster session where all participants can study and discuss the other proposals.
+    o For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project. The hope is that the last project can be aligned with your interests/research project(s).
+    o If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course
     o Based on feedback etc, each group finalizes the report and submits for grading. 
   * Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course. 
 !eblock