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diff --git a/Kaggle/NIPS/mchrzasz.pdf b/Kaggle/NIPS/mchrzasz.pdf
index 308057c..57821ae 100644
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diff --git a/Kaggle/NIPS/mchrzasz.tex b/Kaggle/NIPS/mchrzasz.tex
index 5c92421..2c13020 100644
--- a/Kaggle/NIPS/mchrzasz.tex
+++ b/Kaggle/NIPS/mchrzasz.tex
@@ -264,14 +264,14 @@
 {~}	
 	\begin{minipage}{\textwidth}
        {\color{   PineGreen}{$\Rrightarrow$}} HEP meets ML Award\\
-       An award will be given to the team that, as judged by the LHCb collaboration members on the organizing committee, creates a model that is most useful for the LHCb experiment. The selection criteria include:
+       An award will be given to the team that, as judged by the LHCb collaboration members on the organizing committee, creates a model that is the most useful for the LHCb experiment. The selection criteria include:
 \begin{itemize}
 \item The simplicity/straightforwardness/originality of the approach.
 \item The computing requirements (CPU and memory demands).
 \item The suitability for use in production.
 \item The robustness with respect to lack of training statistics.
 \end{itemize}
-The winning team will be awarded $2000~\$ $ and invited to meet the LHCb collaboration physicists at dedicated {\color{PineGreen}{\href{https://indico.cern.ch/event/433556/other-view?view=lhcb_meeting}{workshop}}} held by University of Zurich on Feb. 18-20, 2016.
+The winning team will be awarded $2000~\$ $ and invited to meet the LHCb collaboration physicists at dedicated {\color{PineGreen}{\href{https://indico.cern.ch/event/433556/other-view?view=lhcb_meeting}{workshop}}} held by University of Zurich on Feb. 18-20, 2016. \\ We thank Intel for supporting this prize.
 
 \pause
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@@ -375,13 +375,13 @@
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 {~}\\
 
-{\color{PineGreen}{ $\Rrightarrow$ }} ''In Machine Learning the problem of different data sets is well known,and solution is called Transfer learning. It aims at transferring knowledge from a model created on the train set to the test set, assuming they differ in some aspects, e.g. in distribution.''
+{\color{PineGreen}{ $\Rrightarrow$ }} ''In Machine Learning the problem of different data sets is well known, and solution is called Transfer learning. It aims at transferring knowledge from a model created on the train set to the test set, assuming they differ in some aspects, e.g. in distribution.''
 
-{\color{PineGreen}{ $\Rrightarrow$ }} This solutions really makes us optimistic at it solves problem that we are usually facing in rare decays:
+{\color{PineGreen}{ $\Rrightarrow$ }} This solutions really makes us optimistic as it solves problem that we are usually facing in rare decays:
 \begin{itemize}
 \item Small statistics
 \item Imperfect MC
-\item Translating informations from control to signal channel.
+\item Translating information from control to signal channel.
 \end{itemize}