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update of summary

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mhjensen
2018-11-29 04:13:30 +01:00
parent 0e2bcf8856
commit 0d4eeb2503
17 changed files with 558 additions and 322 deletions
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doc/pub/summary/html/._summary-bs000.html
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<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>
doc/pub/summary/html/._summary-bs001.html
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<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>
doc/pub/summary/html/._summary-bs002.html
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<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>
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<a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_self">Our ideal about knowledge on computational science</a>
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<!-- 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>
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<p>
The course has two central parts
<ol>
<li> Statistical analysis and optimization of data</li>
<li> Machine learning</li>
</ol>
<p>
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doc/pub/summary/html/._summary-bs004.html
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<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">
doc/pub/summary/html/._summary-bs005.html
+24 -14
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<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">
doc/pub/summary/html/._summary-bs006.html
+27 -20
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<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>
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<!-- 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>
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<h2 id="___sec6" class="anchor">Other courses on Data science and Machine Learning at UiO </h2>
<p>
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<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 -->
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<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>
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<center><h4>Nov 29, 2018</h4></center> <!-- date -->
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<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>
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<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>
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<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>
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<section>
<h2 id="___sec4">Learning outcomes and overarching aims of this course </h2>
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<p>
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<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>
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<center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
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<center><h4>Nov 29, 2018</h4></center> <!-- date -->
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<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?
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<p>
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<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>
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Neural networks;
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Decisions trees and nearest neighbor algorithms
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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>
doc/pub/summary/html/summary.html
+89 -63
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@@ -69,22 +43,17 @@ div { text-align: justify; text-justify: inter-word; }
2,
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('What did I learn in school this year?', 2, None, '___sec1'),
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'___sec5'),
('Other courses on Data science and Machine Learning at UiO',
2,
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@@ -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>
doc/pub/summary/ipynb/ipynb-summary-src.tar.gz
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+78 -11
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@@ -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",
doc/pub/summary/pdf/summary-minted.pdf
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doc/src/Summary/summary.do.txt
+59 -12
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@@ -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.