-
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 --> Contents
Our ideal about knowledge on computational science 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 --> Contents
+The course has two central parts + +
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 --> Contents
+The following topics will be covered + +
+The following topics will be covered + +
+
-
+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. + +
-
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 {
-
@@ -150,7 +150,7 @@ td.padding {
Our ideal about knowledge on computational science
@@ -164,36 +164,100 @@ Does that match the experiences you have made this semester?
+The course has two central parts
+
+
+The following topics will be covered
+
+
+The following topics will be covered
+
+
+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.
+
-
+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.
+
+
-
Our ideal about knowledge on computational science
@@ -140,40 +109,97 @@ Does that match the experiences you have made this semester?
+The course has two central parts
+
+
+The following topics will be covered
+
+
+The following topics will be covered
+
+
+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.
+
-
-
-
-
-
-
+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.
+
+
-
Our ideal about knowledge on computational science
@@ -145,40 +114,97 @@ Does that match the experiences you have made this semester?
+The course has two central parts
+
+
+The following topics will be covered
+
+
+The following topics will be covered
+
+
+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.
+
-
-
-
-
-
-
+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.
+
+What did I learn in school this year
+What did I learn in school this year?
Topics we have covered this year
+
+
+Statistical analysis and optimization of data
+
+
+
+Machine learning
+
+
+
+Learning outcomes and overarching aims of this course
+
+
-
-
Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5
-Learning outcomes and overarching aims of this course
-Additional learning outcomes
-Other courses on Data science and Machine Learning at UiO
+
+
+
Nov 28, 2018
Nov 29, 2018
@@ -126,7 +95,7 @@ end of tocinfo -->
-What did I learn in school this year
+What did I learn in school this year?
Topics we have covered this year
+
+
+
+
+
+Statistical analysis and optimization of data
+
+
+
+
+
+
+Machine learning
+
+
+
+
+
+
+Learning outcomes and overarching aims of this course
+
+
-
-
-Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5
-
-
-
-Learning outcomes and overarching aims of this course
-
-
-
-Additional learning outcomes
-
-
Other courses on Data science and Machine Learning at UiO
+
+
Best wishes to you all and thanks so much for your heroic efforts this semester
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; }
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-.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); }
-.alert-warning { background-image: url(https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_images/small_gray_warning.png); }
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div { text-align: justify; text-justify: inter-word; }
@@ -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 -->
Nov 28, 2018
Nov 29, 2018
@@ -131,7 +100,7 @@ end of tocinfo -->
-What did I learn in school this year
+What did I learn in school this year?
Topics we have covered this year
+
+
+
+
+
+Statistical analysis and optimization of data
+
+
+
+
+
+
+Machine learning
+
+
+
+
+
+
+Learning outcomes and overarching aims of this course
+
+
-
-
-Linear algebra and eigenvalue problems, chapters 6.1-6.5 and 7.1-7.5
-
-
-
-Learning outcomes and overarching aims of this course
-
-
-
-Additional learning outcomes
-
-
Other courses on Data science and Machine Learning at UiO
+
+
Best wishes to you all and thanks so much for your heroic efforts this semester
diff --git a/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz b/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz
index 8d6ac9064..8838b7403 100644
Binary files a/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz and b/doc/pub/summary/ipynb/ipynb-summary-src.tar.gz differ
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 @@
" \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