--- a/doc/v2_planning/arch_FB.txt	Tue Oct 05 09:57:35 2010 -0400
+++ b/doc/v2_planning/arch_FB.txt	Tue Oct 05 12:26:02 2010 -0400
@@ -14,10 +14,10 @@
 
 * Select the hyper parameter search space with `jobman sqlschedules`
 * Dispatch the jobs with dbidispatch
-* *Manually*(fixable) reset jobs status to START.
+* *Manually* (fixable) reset jobs status to START.
    * I started it, but I will change the syntax to make it more generic.
 * *Manually* relaunch crashed jobs.
-* *Manually*(fixable) analyse/visualise the result. (We need to start those meeting at some point)
+* *Manually* (fixable) analyse/visualise the result. (We need to start those meeting at some point)
 
 Example MLP+cross validataion
 -----------------------------
@@ -39,7 +39,7 @@
 * *Jobman Extension* We can extend jobman to handle dependency between jobs.
     * Proposed syntax:
 
-.. code-block::
+.. code-block:: bash
 
       jobman sqlschedule p0={{}} ... -- p1={{}} ... -- p2=...
 
@@ -58,7 +58,7 @@
 * *Jobman Policy* All change to the db should be doable by jobman command.
 
 * *Manually* relaunch crashed jobs.
-* *Manually*(fixable) analyse/visualise the result.
+* *Manually* (fixable) analyse/visualise the result.
    * Those tools need to understand the concept of job phase or be agnostic of that.
 
 

--- a/doc/v2_planning/committees.txt	Tue Oct 05 09:57:35 2010 -0400
+++ b/doc/v2_planning/committees.txt	Tue Oct 05 12:26:02 2010 -0400
@@ -1,4 +1,4 @@
-List of committees and their members (leader marked with a *):
+List of committees and their members (leader marked with a \*):
 
 * Existing Python ML libraries investigation: GD, DWF, IG, DE
 * Dataset interface: DE*, OB, OD, AB, PV

--- 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
+

--- a/doc/v2_planning/requirements.txt	Tue Oct 05 09:57:35 2010 -0400
+++ b/doc/v2_planning/requirements.txt	Tue Oct 05 12:26:02 2010 -0400
@@ -134,10 +134,10 @@
 
 R15. If you see the library as a driver that controls several components ( and
 we argue that any approach can be seen like this), the driver should always :
-  - be serializable
-  - respond to internal interrupts ("checkpoints")
-  - respond to external interrupts ( timeout)
-  - async interrupts ( eg. SIGTERM)
+    - be serializable
+    - respond to internal interrupts ("checkpoints")
+    - respond to external interrupts ( timeout)
+    - async interrupts ( eg. SIGTERM)
 
 R16 Cognitive load should be minimal (debatable requirement)
   Notes : Is hard to actually appreciate cognitive load, so this should be