From 0d4eeb25037ccba9ed928ab321b4712de51a96c7 Mon Sep 17 00:00:00 2001
From: mhjensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 29 Nov 2018 04:13:30 +0100
Subject: [PATCH] update of summary

---
 doc/pub/summary/html/._summary-bs000.html     |  23 ++-
 doc/pub/summary/html/._summary-bs001.html     |  21 +--
 doc/pub/summary/html/._summary-bs002.html     |  23 ++-
 doc/pub/summary/html/._summary-bs003.html     |  34 ++--
 doc/pub/summary/html/._summary-bs004.html     |  37 +++--
 doc/pub/summary/html/._summary-bs005.html     |  38 +++--
 doc/pub/summary/html/._summary-bs006.html     |  47 +++---
 doc/pub/summary/html/._summary-bs007.html     |  35 ++--
 doc/pub/summary/html/._summary-bs008.html     |  21 +--
 doc/pub/summary/html/summary-bs.html          |  23 ++-
 doc/pub/summary/html/summary-reveal.html      | 114 ++++++++++---
 doc/pub/summary/html/summary-solarized.html   | 152 ++++++++++--------
 doc/pub/summary/html/summary.html             | 152 ++++++++++--------
 .../summary/ipynb/ipynb-summary-src.tar.gz    | Bin 2872426 -> 2872426 bytes
 doc/pub/summary/ipynb/summary.ipynb           |  89 ++++++++--
 doc/pub/summary/pdf/summary-minted.pdf        | Bin 3078738 -> 3111955 bytes
 doc/src/Summary/summary.do.txt                |  71 ++++++--
 17 files changed, 558 insertions(+), 322 deletions(-)

diff --git a/doc/pub/summary/html/._summary-bs000.html b/doc/pub/summary/html/._summary-bs000.html
index 588acffcb..756a06171 100644
--- a/doc/pub/summary/html/._summary-bs000.html
+++ b/doc/pub/summary/html/._summary-bs000.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -132,7 +127,7 @@ end of tocinfo -->
 <center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 28, 2018</h4></center> <!-- date -->
+<center><h4>Nov 29, 2018</h4></center> <!-- date -->
 <br>
 <p>
 
diff --git a/doc/pub/summary/html/._summary-bs001.html b/doc/pub/summary/html/._summary-bs001.html
index 51efc8346..516cb3988 100644
--- a/doc/pub/summary/html/._summary-bs001.html
+++ b/doc/pub/summary/html/._summary-bs001.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
diff --git a/doc/pub/summary/html/._summary-bs002.html b/doc/pub/summary/html/._summary-bs002.html
index a03ab436e..63b93ac48 100644
--- a/doc/pub/summary/html/._summary-bs002.html
+++ b/doc/pub/summary/html/._summary-bs002.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -113,7 +108,7 @@ end of tocinfo -->
 <a name="part0002"></a>
 <!-- !split -->
 
-<h2 id="___sec1" class="anchor"><a href="https://www.youtube.com/watch?v=ZTxmDV_njsc" target="_self">What did I learn in school this year</a> </h2>
+<h2 id="___sec1" class="anchor">What did I learn in school this year? </h2>
 
 <p>
 <a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_self">Our ideal about knowledge on computational science</a>
diff --git a/doc/pub/summary/html/._summary-bs003.html b/doc/pub/summary/html/._summary-bs003.html
index 8f2ec5b34..63988a0d9 100644
--- a/doc/pub/summary/html/._summary-bs003.html
+++ b/doc/pub/summary/html/._summary-bs003.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -115,12 +110,13 @@ end of tocinfo -->
 
 <h2 id="___sec2" class="anchor">Topics we have covered this year </h2>
 
-<ul>
-<li></li> 
-<li> </li>
-<li> </li>
-<li></li> 
-</ul>
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
 
 <p>
 <!-- navigation buttons at the bottom of the page -->
diff --git a/doc/pub/summary/html/._summary-bs004.html b/doc/pub/summary/html/._summary-bs004.html
index ee42b7e9f..23ac8d38b 100644
--- a/doc/pub/summary/html/._summary-bs004.html
+++ b/doc/pub/summary/html/._summary-bs004.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -113,9 +108,23 @@ end of tocinfo -->
 <a name="part0004"></a>
 <!-- !split -->
 
-<h2 id="___sec3" class="anchor">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5 </h2>
+<h2 id="___sec3" class="anchor">Statistical analysis and optimization of data </h2>
 
 <p>
+The following topics will be covered
+
+<ol>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Central elements from linear algebra</li>
+<li> Gradient methods for data optimization</li>
+<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;</li>
+<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
+<li> Principal Component Analysis.</li>
+</ol>
+
 <p>
 <!-- navigation buttons at the bottom of the page -->
 <ul class="pagination">
diff --git a/doc/pub/summary/html/._summary-bs005.html b/doc/pub/summary/html/._summary-bs005.html
index 97bacc79d..be0e9c6d2 100644
--- a/doc/pub/summary/html/._summary-bs005.html
+++ b/doc/pub/summary/html/._summary-bs005.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -113,9 +108,24 @@ end of tocinfo -->
 <a name="part0005"></a>
 <!-- !split -->
 
-<h2 id="___sec4" class="anchor">Learning outcomes and overarching aims of this course </h2>
+<h2 id="___sec4" class="anchor">Machine learning </h2>
 
 <p>
+The following topics will be covered
+
+<dl>
+<dt>%s<dd>
+  Linear methods for regression and classification;
+<dt>%s<dd>
+  Boltzmann machines;
+<dt>%s<dd>
+  Neural networks;
+<dt>%s<dd>
+  Decisions trees and nearest neighbor algorithms
+<dt>%s<dd>
+  Support vector machines
+</dl>
+
 <p>
 <!-- navigation buttons at the bottom of the page -->
 <ul class="pagination">
diff --git a/doc/pub/summary/html/._summary-bs006.html b/doc/pub/summary/html/._summary-bs006.html
index f27162e38..a2ccdf4e5 100644
--- a/doc/pub/summary/html/._summary-bs006.html
+++ b/doc/pub/summary/html/._summary-bs006.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -113,17 +108,29 @@ end of tocinfo -->
 <a name="part0006"></a>
 <!-- !split -->
 
-<h2 id="___sec5" class="anchor">Additional learning outcomes </h2>
-<div class="panel panel-default">
-<div class="panel-body">
-<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
+<h2 id="___sec5" class="anchor">Learning outcomes and overarching aims of this course </h2>
 
 <p>
-</div>
-</div>
+The course introduces a variety of central algorithms and methods
+essential for studies of data analysis and machine learning. The
+course is project based and through the various projects, normally
+three, you will be exposed to fundamental research problems
+in these fields, with the aim to reproduce state of the art scientific
+results. The students will learn to develop and structure large codes
+for studying these systems, get acquainted with computing facilities
+and learn to handle large scientific projects. A good scientific and
+ethical conduct is emphasized throughout the course. 
 
+<ul>
+<li> Understand linear methods for regression and classification;</li>
+<li> Learn about neural network;</li>
+<li> Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
+</ul>
 
-<p>
 <p>
 <!-- navigation buttons at the bottom of the page -->
 <ul class="pagination">
diff --git a/doc/pub/summary/html/._summary-bs007.html b/doc/pub/summary/html/._summary-bs007.html
index 44dc33dae..301e16bf3 100644
--- a/doc/pub/summary/html/._summary-bs007.html
+++ b/doc/pub/summary/html/._summary-bs007.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -116,6 +111,20 @@ end of tocinfo -->
 <h2 id="___sec6" class="anchor">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
+The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+
+<ol>
+<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
+<li> "STK-INF3000/4000 Selected Topics in Data Science:"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 &#8211; Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
+<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 &#8211; Dyp l&#230;ring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
+</ol>
+
 <p>
 <!-- navigation buttons at the bottom of the page -->
 <ul class="pagination">
diff --git a/doc/pub/summary/html/._summary-bs008.html b/doc/pub/summary/html/._summary-bs008.html
index 236f8bc96..2f703b5b1 100644
--- a/doc/pub/summary/html/._summary-bs008.html
+++ b/doc/pub/summary/html/._summary-bs008.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
diff --git a/doc/pub/summary/html/summary-bs.html b/doc/pub/summary/html/summary-bs.html
index 588acffcb..756a06171 100644
--- a/doc/pub/summary/html/summary-bs.html
+++ b/doc/pub/summary/html/summary-bs.html
@@ -44,22 +44,17 @@ Automatically generated HTML file from DocOnce source
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -91,11 +86,11 @@ end of tocinfo -->
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">"What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
-     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Additional learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
+     <!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
      <!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
 
@@ -132,7 +127,7 @@ end of tocinfo -->
 <center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 28, 2018</h4></center> <!-- date -->
+<center><h4>Nov 29, 2018</h4></center> <!-- date -->
 <br>
 <p>
 
diff --git a/doc/pub/summary/html/summary-reveal.html b/doc/pub/summary/html/summary-reveal.html
index 0136d0b1f..f9f575baf 100644
--- a/doc/pub/summary/html/summary-reveal.html
+++ b/doc/pub/summary/html/summary-reveal.html
@@ -132,7 +132,7 @@ td.padding {
 <center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Nov 28, 2018</h4></center> <!-- date -->
+<center><h4>Nov 29, 2018</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -150,7 +150,7 @@ td.padding {
 
 
 <section>
-<h2 id="___sec1"><a href="https://www.youtube.com/watch?v=ZTxmDV_njsc" target="_blank">What did I learn in school this year</a> </h2>
+<h2 id="___sec1">What did I learn in school this year? </h2>
 
 <p>
 <a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_blank">Our ideal about knowledge on computational science</a>
@@ -164,36 +164,100 @@ Does that match the experiences you have made this semester?
 <section>
 <h2 id="___sec2">Topics we have covered this year </h2>
 
+<p>
+The course has two central parts
+
+<ol>
+<p><li> Statistical analysis and optimization of data</li>
+<p><li> Machine learning</li>
+</ol>
+</section>
+
+
+<section>
+<h2 id="___sec3">Statistical analysis and optimization of data </h2>
+
+<p>
+The following topics will be covered
+
+<ol>
+<p><li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<p><li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<p><li> Central elements of Bayesian statistics and modeling;</li>
+<p><li> Central elements from linear algebra</li>
+<p><li> Gradient methods for data optimization</li>
+<p><li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;</li>
+<p><li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<p><li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
+<p><li> Principal Component Analysis.</li>
+</ol>
+</section>
+
+
+<section>
+<h2 id="___sec4">Machine learning </h2>
+
+<p>
+The following topics will be covered
+
+<dl>
+<dt>%s<dd>
+  Linear methods for regression and classification;
+<dt>%s<dd>
+  Boltzmann machines;
+<dt>%s<dd>
+  Neural networks;
+<dt>%s<dd>
+  Decisions trees and nearest neighbor algorithms
+<dt>%s<dd>
+  Support vector machines
+</dl>
+</section>
+
+
+<section>
+<h2 id="___sec5">Learning outcomes and overarching aims of this course </h2>
+
+<p>
+The course introduces a variety of central algorithms and methods
+essential for studies of data analysis and machine learning. The
+course is project based and through the various projects, normally
+three, you will be exposed to fundamental research problems
+in these fields, with the aim to reproduce state of the art scientific
+results. The students will learn to develop and structure large codes
+for studying these systems, get acquainted with computing facilities
+and learn to handle large scientific projects. A good scientific and
+ethical conduct is emphasized throughout the course. 
+
 <ul>
-<p><li></li> 
-<p><li> </li>
-<p><li> </li>
-<p><li></li> 
+<p><li> Understand linear methods for regression and classification;</li>
+<p><li> Learn about neural network;</li>
+<p><li> Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;</li>
+<p><li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<p><li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<p><li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;</li>
+<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
 </ul>
 </section>
 
 
-<section>
-<h2 id="___sec3">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5 </h2>
-</section>
-
-
-<section>
-<h2 id="___sec4">Learning outcomes and overarching aims of this course </h2>
-</section>
-
-
-<section>
-<h2 id="___sec5">Additional learning outcomes </h2>
-<div class="alert alert-block alert-block alert-text-normal">
-<b></b>
-<p>
-</div>
-</section>
-
-
 <section>
 <h2 id="___sec6">Other courses on Data science and Machine Learning  at UiO </h2>
+
+<p>
+The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+
+<ol>
+<p><li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
+<p><li> "STK-INF3000/4000 Selected Topics in Data Science:"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.</li> 
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 &#8211; Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
+<p><li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
+<p><li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 &#8211; Dyp l&#230;ring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
+</ol>
 </section>
 
 
diff --git a/doc/pub/summary/html/summary-solarized.html b/doc/pub/summary/html/summary-solarized.html
index 95e6f48e5..ac1894905 100644
--- a/doc/pub/summary/html/summary-solarized.html
+++ b/doc/pub/summary/html/summary-solarized.html
@@ -25,32 +25,6 @@ pre {
   border: 0pt solid #93a1a1;
   box-shadow: none;
 }
-.alert-text-small   { font-size: 80%;  }
-.alert-text-large   { font-size: 130%; }
-.alert-text-normal  { font-size: 90%;  }
-.alert {
-  padding:8px 35px 8px 14px; margin-bottom:18px;
-  text-shadow:0 1px 0 rgba(255,255,255,0.5);
-  border:1px solid #93a1a1;
-  border-radius: 4px;
-  -webkit-border-radius: 4px;
-  -moz-border-radius: 4px;
-  color: #555;
-  background-color: #eee8d5;
-  background-position: 10px 5px;
-  background-repeat: no-repeat;
-  background-size: 38px;
-  padding-left: 55px;
-  width: 75%;
- }
-.alert-block {padding-top:14px; padding-bottom:14px}
-.alert-block > p, .alert-block > ul {margin-bottom:1em}
-.alert li {margin-top: 1em}
-.alert-block p+p {margin-top:5px}
-.alert-notice { background-image: url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_yellow_notice.png); }
-.alert-summary  { background-image:url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_yellow_summary.png); }
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 div { text-align: justify; text-justify: inter-word; }
 </style>
@@ -64,22 +38,17 @@ div { text-align: justify; text-justify: inter-word; }
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -114,7 +83,7 @@ end of tocinfo -->
 <center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 28, 2018</h4></center> <!-- date -->
+<center><h4>Nov 29, 2018</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -126,7 +95,7 @@ end of tocinfo -->
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec1"><a href="https://www.youtube.com/watch?v=ZTxmDV_njsc" target="_blank">What did I learn in school this year</a> </h2>
+<h2 id="___sec1">What did I learn in school this year? </h2>
 
 <p>
 <a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_blank">Our ideal about knowledge on computational science</a>
@@ -140,40 +109,97 @@ Does that match the experiences you have made this semester?
 
 <h2 id="___sec2">Topics we have covered this year </h2>
 
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec3">Statistical analysis and optimization of data </h2>
+
+<p>
+The following topics will be covered
+
+<ol>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Central elements from linear algebra</li>
+<li> Gradient methods for data optimization</li>
+<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;</li>
+<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
+<li> Principal Component Analysis.</li>
+</ol>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec4">Machine learning </h2>
+
+<p>
+The following topics will be covered
+
+<dl>
+<dt>%s<dd>
+  Linear methods for regression and classification;
+<dt>%s<dd>
+  Boltzmann machines;
+<dt>%s<dd>
+  Neural networks;
+<dt>%s<dd>
+  Decisions trees and nearest neighbor algorithms
+<dt>%s<dd>
+  Support vector machines
+</dl>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec5">Learning outcomes and overarching aims of this course </h2>
+
+<p>
+The course introduces a variety of central algorithms and methods
+essential for studies of data analysis and machine learning. The
+course is project based and through the various projects, normally
+three, you will be exposed to fundamental research problems
+in these fields, with the aim to reproduce state of the art scientific
+results. The students will learn to develop and structure large codes
+for studying these systems, get acquainted with computing facilities
+and learn to handle large scientific projects. A good scientific and
+ethical conduct is emphasized throughout the course. 
+
 <ul>
-<li></li> 
-<li> </li>
-<li> </li>
-<li></li> 
+<li> Understand linear methods for regression and classification;</li>
+<li> Learn about neural network;</li>
+<li> Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
 </ul>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec3">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5 </h2>
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec4">Learning outcomes and overarching aims of this course </h2>
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec5">Additional learning outcomes </h2>
-<div class="alert alert-block alert-block alert-text-normal">
-<b></b>
-<p>
-
-
-</div>
-
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
 <h2 id="___sec6">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
+The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+
+<ol>
+<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
+<li> "STK-INF3000/4000 Selected Topics in Data Science:"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 &#8211; Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
+<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 &#8211; Dyp l&#230;ring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
+</ol>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
 <h2 id="___sec7">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
diff --git a/doc/pub/summary/html/summary.html b/doc/pub/summary/html/summary.html
index ffa874261..0b5071d6a 100644
--- a/doc/pub/summary/html/summary.html
+++ b/doc/pub/summary/html/summary.html
@@ -30,32 +30,6 @@ p { text-indent: 0px; }
 hr { border: 0; width: 80%; border-bottom: 1px solid #aaa}
 p.caption { width: 80%; font-style: normal; text-align: left; }
 hr.figure { border: 0; width: 80%; border-bottom: 1px solid #aaa}
-.alert-text-small   { font-size: 80%;  }
-.alert-text-large   { font-size: 130%; }
-.alert-text-normal  { font-size: 90%;  }
-.alert {
-  padding:8px 35px 8px 14px; margin-bottom:18px;
-  text-shadow:0 1px 0 rgba(255,255,255,0.5);
-  border:1px solid #bababa;
-  border-radius: 4px;
-  -webkit-border-radius: 4px;
-  -moz-border-radius: 4px;
-  color: #555;
-  background-color: #f8f8f8;
-  background-position: 10px 5px;
-  background-repeat: no-repeat;
-  background-size: 38px;
-  padding-left: 55px;
-  width: 75%;
- }
-.alert-block {padding-top:14px; padding-bottom:14px}
-.alert-block > p, .alert-block > ul {margin-bottom:1em}
-.alert li {margin-top: 1em}
-.alert-block p+p {margin-top:5px}
-.alert-notice { background-image: url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_gray_notice.png); }
-.alert-summary  { background-image:url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_gray_summary.png); }
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-.alert-question {background-image:url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_gray_question.png); }
 
 div { text-align: justify; text-justify: inter-word; }
 </style>
@@ -69,22 +43,17 @@ div { text-align: justify; text-justify: inter-word; }
                2,
                None,
                '___sec0'),
-              ('"What did I learn in school this '
-               'year":"https://www.youtube.com/watch?v=ZTxmDV_njsc"',
-               2,
-               None,
-               '___sec1'),
+              ('What did I learn in school this year?', 2, None, '___sec1'),
               ('Topics we have covered this year', 2, None, '___sec2'),
-              ('Linear algebra and eigenvalue problems, chapters 6.1-6.5 and '
-               '7.1-7.5',
+              ('Statistical analysis and optimization of data',
                2,
                None,
                '___sec3'),
+              ('Machine learning', 2, None, '___sec4'),
               ('Learning outcomes and overarching aims of this course',
                2,
                None,
-               '___sec4'),
-              ('Additional learning outcomes', 2, None, '___sec5'),
+               '___sec5'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
@@ -119,7 +88,7 @@ end of tocinfo -->
 <center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 28, 2018</h4></center> <!-- date -->
+<center><h4>Nov 29, 2018</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -131,7 +100,7 @@ end of tocinfo -->
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec1"><a href="https://www.youtube.com/watch?v=ZTxmDV_njsc" target="_blank">What did I learn in school this year</a> </h2>
+<h2 id="___sec1">What did I learn in school this year? </h2>
 
 <p>
 <a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_blank">Our ideal about knowledge on computational science</a>
@@ -145,40 +114,97 @@ Does that match the experiences you have made this semester?
 
 <h2 id="___sec2">Topics we have covered this year </h2>
 
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec3">Statistical analysis and optimization of data </h2>
+
+<p>
+The following topics will be covered
+
+<ol>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Central elements from linear algebra</li>
+<li> Gradient methods for data optimization</li>
+<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;</li>
+<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
+<li> Principal Component Analysis.</li>
+</ol>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec4">Machine learning </h2>
+
+<p>
+The following topics will be covered
+
+<dl>
+<dt>%s<dd>
+  Linear methods for regression and classification;
+<dt>%s<dd>
+  Boltzmann machines;
+<dt>%s<dd>
+  Neural networks;
+<dt>%s<dd>
+  Decisions trees and nearest neighbor algorithms
+<dt>%s<dd>
+  Support vector machines
+</dl>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec5">Learning outcomes and overarching aims of this course </h2>
+
+<p>
+The course introduces a variety of central algorithms and methods
+essential for studies of data analysis and machine learning. The
+course is project based and through the various projects, normally
+three, you will be exposed to fundamental research problems
+in these fields, with the aim to reproduce state of the art scientific
+results. The students will learn to develop and structure large codes
+for studying these systems, get acquainted with computing facilities
+and learn to handle large scientific projects. A good scientific and
+ethical conduct is emphasized throughout the course. 
+
 <ul>
-<li></li> 
-<li> </li>
-<li> </li>
-<li></li> 
+<li> Understand linear methods for regression and classification;</li>
+<li> Learn about neural network;</li>
+<li> Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
 </ul>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec3">Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5 </h2>
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec4">Learning outcomes and overarching aims of this course </h2>
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec5">Additional learning outcomes </h2>
-<div class="alert alert-block alert-block alert-text-normal">
-<b></b>
-<p>
-
-
-</div>
-
-
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
 <h2 id="___sec6">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
+The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+
+<ol>
+<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
+<li> "STK-INF3000/4000 Selected Topics in Data Science:"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 &#8211; Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
+<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 &#8211; Dyp l&#230;ring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
+</ol>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
 <h2 id="___sec7">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
diff --git a/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz b/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz
index 8d6ac90648aaaf3e1ea535e38837d86588009f3a..8838b7403d348fd626fd2860b747dd20b99b1ef1 100644
GIT binary patch
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Hxjlb>TM{C?

delta 148
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diff --git a/doc/pub/summary/ipynb/summary.ipynb b/doc/pub/summary/ipynb/summary.ipynb
index c8c4a6149..eb2a8a269 100644
--- a/doc/pub/summary/ipynb/summary.ipynb
+++ b/doc/pub/summary/ipynb/summary.ipynb
@@ -10,7 +10,7 @@
     "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen   Email morten.hjorth-jensen@fys.uio.no**, Department of Physics and Center of Mathematics for Applications, University of Oslo and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Nov 28, 2018**\n",
+    "Date: **Nov 29, 2018**\n",
     "\n",
     "Copyright 1999-2018, Morten Hjorth-Jensen   Email morten.hjorth-jensen@fys.uio.no. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -36,7 +36,7 @@
     "\n",
     "\n",
     "\n",
-    "## [What did I learn in school this year](https://www.youtube.com/watch?v=ZTxmDV_njsc)\n",
+    "## What did I learn in school this year?\n",
     "\n",
     "[Our ideal about knowledge on computational science](http://hplgit.github.io/edu/py_vs_m/computing_competence.html)\n",
     "\n",
@@ -52,37 +52,104 @@
     "\n",
     "\n",
     "## Topics we have covered this year\n",
-    "* \n",
     "\n",
-    "* \n",
+    "The course has two central parts\n",
     "\n",
-    "* \n",
+    "1. Statistical analysis and optimization of data\n",
     "\n",
-    "* \n",
+    "2. Machine learning\n",
     "\n",
-    "## Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5\n",
+    "## Statistical analysis and optimization of data\n",
     "\n",
+    "The following topics will be covered\n",
+    "1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;\n",
+    "\n",
+    "2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;\n",
+    "\n",
+    "3. Central elements of Bayesian statistics and modeling;\n",
+    "\n",
+    "4. Central elements from linear algebra\n",
+    "\n",
+    "5. Gradient methods for data optimization\n",
+    "\n",
+    "6. Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;\n",
+    "\n",
+    "7. Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;\n",
+    "\n",
+    "8. Practical optimization using Singular-value decomposition and least squares for parameterizing data.\n",
+    "\n",
+    "9. Principal Component Analysis.\n",
+    "\n",
+    "## Machine learning\n",
+    "\n",
+    "The following topics will be covered\n",
+    "%s\n",
+    "  :   \n",
+    "  Linear methods for regression and classification;\n",
+    "\n",
+    "%s\n",
+    "  :   \n",
+    "  Boltzmann machines;\n",
+    "\n",
+    "%s\n",
+    "  :   \n",
+    "  Neural networks;\n",
+    "\n",
+    "%s\n",
+    "  :   \n",
+    "  Decisions trees and nearest neighbor algorithms\n",
+    "\n",
+    "%s\n",
+    "  :   \n",
+    "  Support vector machines\n",
     "\n",
     "## Learning outcomes and overarching aims of this course\n",
     "\n",
+    "The course introduces a variety of central algorithms and methods\n",
+    "essential for studies of data analysis and machine learning. The\n",
+    "course is project based and through the various projects, normally\n",
+    "three, you will be exposed to fundamental research problems\n",
+    "in these fields, with the aim to reproduce state of the art scientific\n",
+    "results. The students will learn to develop and structure large codes\n",
+    "for studying these systems, get acquainted with computing facilities\n",
+    "and learn to handle large scientific projects. A good scientific and\n",
+    "ethical conduct is emphasized throughout the course. \n",
     "\n",
+    "* Understand linear methods for regression and classification;\n",
     "\n",
+    "* Learn about neural network;\n",
     "\n",
-    "## Additional learning outcomes\n",
-    "\n",
-    "\n",
-    "\n",
+    "* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;\n",
     "\n",
+    "* Be capable of extending the acquired knowledge to other systems and cases;\n",
     "\n",
+    "* Have an understanding of central algorithms used in data analysis and machine learning;\n",
     "\n",
+    "* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;\n",
     "\n",
+    "* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.\n",
     "\n",
     "## Other courses on Data science and Machine Learning  at UiO\n",
     "\n",
+    "The link here <https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/>  gives an excellent overview of courses on Machine learning at UiO.\n",
     "\n",
+    "1. [STK2100 Machine learning and statistical methods for prediction and classification](http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html). \n",
     "\n",
+    "2. [IN3050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI with an algorithmic approach. \n",
     "\n",
+    "3. \"STK-INF3000/4000 Selected Topics in Data Science:\"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html\". The course provides insight into selected contemporary relevant topics within Data Science. \n",
     "\n",
+    "4. [IN4080 Natural Language Processing](https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html). Probabilistic and machine learning techniques applied to natural language processing. \n",
+    "\n",
+    "5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n",
+    "\n",
+    "6. [INF4490 Biologically Inspired Computing](http://www.uio.no/studier/emner/matnat/ifi/INF4490/). An introduction to self-adapting methods also called artificial intelligence or machine learning. \n",
+    "\n",
+    "7. [IN-STK5000  Adaptive Methods for Data-Based Decision Making](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n",
+    "\n",
+    "8. [IN5400/INF5860 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n",
+    "\n",
+    "9. [TEK5040 – Dyp læring for autonome systemer](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.\n",
     "\n",
     "## Best wishes to you all and thanks so much for your heroic efforts this semester\n",
     "\n",
diff --git a/doc/pub/summary/pdf/summary-minted.pdf b/doc/pub/summary/pdf/summary-minted.pdf
index b938aff5f664176b47c980460724bbb58bbb5c4d..9b9b07418878a3ed4c21c30148255dda96850a36 100644
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diff --git a/doc/src/Summary/summary.do.txt b/doc/src/Summary/summary.do.txt
index 34121c2a6..e1d048456 100644
--- a/doc/src/Summary/summary.do.txt
+++ b/doc/src/Summary/summary.do.txt
@@ -10,7 +10,7 @@ FIGURE: [figures/whatmeworry.jpeg, width=500 frac=0.6]
 
 
 !split
-===== "What did I learn in school this year":"https://www.youtube.com/watch?v=ZTxmDV_njsc" =====
+===== What did I learn in school this year? =====
 
 "Our ideal about knowledge on computational science":"http://hplgit.github.io/edu/py_vs_m/computing_competence.html"
 
@@ -20,34 +20,81 @@ FIGURE: [figures/exam2.jpg, width=500 frac=0.7]
 
 !split
 ===== Topics we have covered this year =====
-* 
-* 
-* 
-* 
+
+The course has two central parts
+
+o Statistical analysis and optimization of data
+o Machine learning
 
 !split
-===== Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5 =====
+===== Statistical analysis and optimization of data =====
+
+The following topics will be covered
+o Basic concepts, expectation values, variance, covariance, correlation functions and errors;
+o Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
+o Central elements of Bayesian statistics and modeling;
+o Central elements from linear algebra
+o Gradient methods for data optimization
+o Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
+o Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
+o Practical optimization using Singular-value decomposition and least squares for parameterizing data.
+o Principal Component Analysis.
+
+!split
+===== Machine learning =====
+
+The following topics will be covered
+- Linear methods for regression and classification;
+- Boltzmann machines;
+- Neural networks;
+- Decisions trees and nearest neighbor algorithms
+- Support vector machines
 
 
 !split
 ===== Learning outcomes and overarching aims of this course =====
 
+The course introduces a variety of central algorithms and methods
+essential for studies of data analysis and machine learning. The
+course is project based and through the various projects, normally
+three, you will be exposed to fundamental research problems
+in these fields, with the aim to reproduce state of the art scientific
+results. The students will learn to develop and structure large codes
+for studying these systems, get acquainted with computing facilities
+and learn to handle large scientific projects. A good scientific and
+ethical conduct is emphasized throughout the course. 
+
+* Understand linear methods for regression and classification;
+* Learn about neural network;
+* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
+* Be capable of extending the acquired knowledge to other systems and cases;
+* Have an understanding of central algorithms used in data analysis and machine learning;
+* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;
+* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
 
 
 
-!split
-===== Additional learning outcomes =====
-!bblock 
-
-!eblock
-
 
 
 
 !split
 ===== Other courses on Data science and Machine Learning  at UiO =====
 
+The link here URL:"https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/"  gives an excellent overview of courses on Machine learning at UiO.
 
+o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html". 
+
+o "IN3050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach. 
+
+o "STK-INF3000/4000 Selected Topics in Data Science:"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science. 
+
+o "IN4080 Natural Language Processing":"https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html". Probabilistic and machine learning techniques applied to natural language processing. 
+
+o "STK-IN4300 – Statistical learning methods in Data Science":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html". An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
+o "INF4490 Biologically Inspired Computing":"http://www.uio.no/studier/emner/matnat/ifi/INF4490/". An introduction to self-adapting methods also called artificial intelligence or machine learning. 
+o "IN-STK5000  Adaptive Methods for Data-Based Decision Making":"https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html". Methods for adaptive collection and processing of data based on machine learning techniques. 
+o "IN5400/INF5860 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/". An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
+o "TEK5040 – Dyp læring for autonome systemer":"https://www.uio.no/studier/emner/matnat/its/TEK5040/". The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.