update README

Author: riscy

README.org

@@ -1,19 +1,24 @@
 #+TITLE: Machine Learning with Linear Models - a demo
 
+[[file:img/example_results.png]]
+
 * Table of Contents :TOC_4_gh:noexport:
  - [[#description][Description]]
  - [[#usage][Usage]]
 
 * Description
-
   This is a small demo meant to showcase a few simple but different linear
-  models for mapping X to Y.  Different assumptions about the data can lead to
-  (sometimes drastically) different levels of performance.  For instance, when
-  the mapping from X to Y is low rank (i.e., an information 'bottleneck'), a
-  technique called reduced rank regression (RRR) can outperform standard
-  multivariate linear regression (LR).  When the mapping from X to Y is time
-  dependent and based on an underlying linear dynamical system, applying a
-  system identification technique (SUBID) can result in big gains over LR.
+  models for mapping vectors of observations X to vectors of outcomes Y.
+  Different assumptions about the data can lead to different levels of
+  performance -- sometimes drastically.
+
+  For instance, when the mapping from X to Y is low rank (i.e., an information
+  'bottleneck'), a technique called reduced rank regression
+  (~reduced_rank_regressor.py~) can outperform standard multivariate linear
+  regression (~multivariate_regressor.py~).  When the mapping from X to Y is
+  time dependent and based on an underlying linear dynamical system, applying a
+  system identification technique (~system_identifier.py~) can result in big
+  gains over both.
 
   My Ph.D. supervisor Dr. Michael Bowling introduced me to RRR; Dr. Tijl De Bie
   gave a great talk on subspace system identification in 2005 that I modeled my