merge w/ master + 2nd round of review of 01 intro script

.gitignore

 .ipynb_checkpoints/
-.*venv*
+.*venv/

course_layout.md

@@ -93,7 +93,7 @@ TBD: prepare coding session
   - learn regressor for movie scores.
 
 
-## Part 3: accuracy, F1, ROC, ...
+## Part 4: accuracy, F1, ROC, ...
 
 Intention: accuracy is usefull but has pitfalls
 
@@ -104,7 +104,7 @@ Intention: accuracy is usefull but has pitfalls
   - classifier accuracy:
     - confusion matrix
     - accurarcy
-    - pitfalls for unbalanced data sets
+    - pitfalls for unbalanced data sets~
         e.g. diagnose HIV
     - precision / recall
     - ROC ?
@@ -117,7 +117,9 @@ Intention: accuracy is usefull but has pitfalls
 
 - fool them: give them other dataset where classifier fails.
 
-## Part 4: underfitting/overfitting
+## Part 3: underfitting/overfitting
+
+needs: simple accuracy measure.
 
 classifiers / regressors have parameters / degrees of freedom.