--- a/doc/v2_planning/existing_python_ml_libraries.txt	Tue Oct 05 09:57:35 2010 -0400
+++ b/doc/v2_planning/existing_python_ml_libraries.txt	Tue Oct 05 12:26:02 2010 -0400
@@ -101,26 +101,27 @@
 libraries we should definitely be interested in, such as libsvm
 (because it is well-established) and others that get state of the art
 performance or are good for extremely large datasets, etc.
-milk:
-      k-means
-      svm's with arbitrary python types for kernel arguments
-pybrain:
-      lstm
-mlpy:
-      feature selection
-mdp:
-      ica
-      LLE
-scikit.learn:
-      lasso
-      nearest neighbor
-      isomap
-      various metrics
-      mean shift
-      cross validation
-      LDA
-      HMMs
-Yet Another Python Graph Library:
-      graph similarity functions that could be useful if we want to
-learn with graphs as data
 
+* milk:
+    * k-means
+    * svm's with arbitrary python types for kernel arguments
+* pybrain:
+    * lstm
+* mlpy:
+    * feature selection
+* mdp:
+    * ica
+    * LLE
+* scikit.learn:
+    * lasso
+    * nearest neighbor
+    * isomap
+    * various metrics
+    * mean shift
+    * cross validation
+    * LDA
+    * HMMs
+* Yet Another Python Graph Library:
+    * graph similarity functions that could be useful if we want to
+      learn with graphs as data
+