lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update of first week

Browse Source
This commit is contained in:
Morten Hjorth-Jensen
2021-08-23 08:48:47 +02:00
parent 7253396bc5
commit 7c3a4adcaa
7 changed files with 223 additions and 99 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/week34/html/week34-bs.html
+41 -39
View File
@@ -42,6 +42,7 @@ Automatically generated HTML file from DocOnce source
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of first week', 2, None, 'overview-of-first-week'),
('Reading Recommendations', 2, None, 'reading-recommendations'),
('Thursday August 26', 2, None, 'thursday-august-26'),
('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
('Course Format', 2, None, 'course-format'),
@@ -109,7 +110,7 @@ Automatically generated HTML file from DocOnce source
('Meet the Pandas', 2, None, 'meet-the-pandas'),
('Friday August 21', 2, None, 'friday-august-21'),
('Reading Data and fitting', 2, None, 'reading-data-and-fitting'),
('Friday August 21', 2, None, 'friday-august-21'),
('Friday August 27', 2, None, 'friday-august-27'),
('Simple linear regression model using _scikit-learn_',
3,
None,
@@ -167,42 +168,43 @@ MathJax.Hub.Config({
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="._week34-bs001.html#overview-of-first-week" style="font-size: 80%;"><b>Overview of first week</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs002.html#thursday-august-26" style="font-size: 80%;"><b>Thursday August 26</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs003.html#lectures-and-computerlab" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs004.html#course-format" style="font-size: 80%;"><b>Course Format</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs005.html#teachers" style="font-size: 80%;"><b>Teachers</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs006.html#deadlines-for-projects-tentative" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs007.html#recommended-textbooks" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs008.html#prerequisites" style="font-size: 80%;"><b>Prerequisites</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs009.html#learning-outcomes" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs010.html#topics-covered-in-this-course-statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs011.html#topics-covered-in-this-course-machine-learning" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs012.html#extremely-useful-tools-strongly-recommended" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs013.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs014.html#introduction" style="font-size: 80%;"><b>Introduction</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs015.html#what-is-machine-learning" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs016.html#types-of-machine-learning" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs017.html#software-and-needed-installations" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs018.html#python-installers" style="font-size: 80%;"><b>Python installers</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs019.html#useful-python-libraries" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs020.html#installing-r-c-cython-or-julia" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs021.html#installing-r-c-cython-numba-etc" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs022.html#numpy-examples-and-important-matrix-and-vector-handling-packages" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs023.html#basic-matrix-features" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs024.html#some-famous-matrices" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Some famous Matrices</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs025.html#more-basic-matrix-features" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;More Basic Matrix Features</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs026.html#numpy-and-arrays" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs027.html#matrices-in-python" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs028.html#meet-the-pandas" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs031.html#friday-august-21" style="font-size: 80%;"><b>Friday August 21</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs030.html#reading-data-and-fitting" style="font-size: 80%;"><b>Reading Data and fitting</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs031.html#friday-august-21" style="font-size: 80%;"><b>Friday August 21</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#simple-linear-regression-model-using-_scikit-learn_" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Simple linear regression model using <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#organizing-our-data" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Organizing our data</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#seeing-the-wood-for-the-trees" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Seeing the wood for the trees</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#and-what-about-using-neural-networks" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;And what about using neural networks?</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#a-first-summary" style="font-size: 80%;"><b>A first summary</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs002.html#reading-recommendations" style="font-size: 80%;"><b>Reading Recommendations</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs003.html#thursday-august-26" style="font-size: 80%;"><b>Thursday August 26</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs004.html#lectures-and-computerlab" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs005.html#course-format" style="font-size: 80%;"><b>Course Format</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs006.html#teachers" style="font-size: 80%;"><b>Teachers</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs007.html#deadlines-for-projects-tentative" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs008.html#recommended-textbooks" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs009.html#prerequisites" style="font-size: 80%;"><b>Prerequisites</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs010.html#learning-outcomes" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs011.html#topics-covered-in-this-course-statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs012.html#topics-covered-in-this-course-machine-learning" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs013.html#extremely-useful-tools-strongly-recommended" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs014.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs015.html#introduction" style="font-size: 80%;"><b>Introduction</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs016.html#what-is-machine-learning" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs017.html#types-of-machine-learning" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs018.html#software-and-needed-installations" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs019.html#python-installers" style="font-size: 80%;"><b>Python installers</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs020.html#useful-python-libraries" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs021.html#installing-r-c-cython-or-julia" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs022.html#installing-r-c-cython-numba-etc" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs023.html#numpy-examples-and-important-matrix-and-vector-handling-packages" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs024.html#basic-matrix-features" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs025.html#some-famous-matrices" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Some famous Matrices</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs026.html#more-basic-matrix-features" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;More Basic Matrix Features</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs027.html#numpy-and-arrays" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs028.html#matrices-in-python" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs029.html#meet-the-pandas" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs030.html#friday-august-21" style="font-size: 80%;"><b>Friday August 21</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs031.html#reading-data-and-fitting" style="font-size: 80%;"><b>Reading Data and fitting</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs032.html#friday-august-27" style="font-size: 80%;"><b>Friday August 27</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#simple-linear-regression-model-using-_scikit-learn_" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Simple linear regression model using <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#organizing-our-data" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Organizing our data</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#seeing-the-wood-for-the-trees" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Seeing the wood for the trees</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#and-what-about-using-neural-networks" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;And what about using neural networks?</a></li>
<!-- navigation toc: --> <li><a href="._week34-bs033.html#a-first-summary" style="font-size: 80%;"><b>A first summary</b></a></li>
</ul>
</li>
@@ -237,7 +239,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Aug 8, 2021</h4></center> <!-- date -->
<center><h4>Aug 23, 2021</h4></center> <!-- date -->
<br>
<p>
@@ -261,7 +263,7 @@ MathJax.Hub.Config({
<li><a href="._week34-bs008.html">9</a></li>
<li><a href="._week34-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._week34-bs032.html">33</a></li>
<li><a href="._week34-bs033.html">34</a></li>
<li><a href="._week34-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week34/html/week34-reveal.html
+39 -12
View File
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Aug 8, 2021</h4></center> <!-- date -->
<center><h4>Aug 23, 2021</h4></center> <!-- date -->
<br>
<p>
@@ -166,6 +166,8 @@ MathJax.Hub.Config({
<b></b>
<ul>
<p><li> Wednesday August 25: Introduction to software and repetition of Python Programming</li>
<p><li> Thursday August 26: First lecture: Presentation of the course, aims and content</li>
<p><li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
@@ -178,11 +180,36 @@ MathJax.Hub.Config({
</section>
<section>
<h2 id="reading-recommendations">Reading Recommendations </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
For the reading assignments we use the following abbreviations:
<ul>
<p><li> GBC: Goodfellow, Bengio, and Courville, Deep Learning</li>
<p><li> CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning</li>
<p><li> HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</li>
<p><li> AG: Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow</li>
</ul>
<p>
Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a>
</div>
</section>
<section>
<h2 id="thursday-august-26">Thursday August 26 </h2>
<p>
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a>.
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from Fall Semester 2020</a>.
<p>
The lectures will be recorded and updated videos will be posted after the lectures.
</section>
@@ -256,7 +283,7 @@ MathJax.Hub.Config({
<p><li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li>
<p><li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
<p><li> <b>Office hours</b>: <em>Anytime</em>! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.</li>
</ul>
<p><li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
@@ -307,10 +334,10 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
</ol>
<p>
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.
<ol>
<p><li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a> This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.</li>
<p><li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<p><li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
<p>
@@ -835,14 +862,14 @@ $$
<p>
<table border="1">
<thead>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
</thead>
<tbody>
<tr><td align="center"> \( A = A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij} = a_{ji} \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A = A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij} = a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij} = a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij}=a_{ji} \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij}=a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij}=a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) </td> </tr>
</tbody>
</table>
@@ -1315,7 +1342,7 @@ But before we really start with nuclear physics data, let's just look at some si
<section>
<h2 id="friday-august-21">Friday August 21 </h2>
<h2 id="friday-august-27">Friday August 27 </h2>
</section>
doc/pub/week34/html/week34-solarized.html
+40 -13
View File
@@ -62,6 +62,7 @@ div { text-align: justify; text-justify: inter-word; }
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of first week', 2, None, 'overview-of-first-week'),
('Reading Recommendations', 2, None, 'reading-recommendations'),
('Thursday August 26', 2, None, 'thursday-august-26'),
('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
('Course Format', 2, None, 'course-format'),
@@ -129,7 +130,7 @@ div { text-align: justify; text-justify: inter-word; }
('Meet the Pandas', 2, None, 'meet-the-pandas'),
('Friday August 21', 2, None, 'friday-august-21'),
('Reading Data and fitting', 2, None, 'reading-data-and-fitting'),
('Friday August 21', 2, None, 'friday-august-21'),
('Friday August 27', 2, None, 'friday-august-27'),
('Simple linear regression model using _scikit-learn_',
3,
None,
@@ -190,7 +191,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Aug 8, 2021</h4></center> <!-- date -->
<center><h4>Aug 23, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -203,6 +204,7 @@ MathJax.Hub.Config({
<p>
<ul>
<li> Wednesday August 25: Introduction to software and repetition of Python Programming</li>
<li> Thursday August 26: First lecture: Presentation of the course, aims and content</li>
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
<li> Friday August 27: Linear regression</li>
@@ -211,13 +213,38 @@ MathJax.Hub.Config({
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="reading-recommendations">Reading Recommendations </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
For the reading assignments we use the following abbreviations:
<ul>
<li> GBC: Goodfellow, Bengio, and Courville, Deep Learning</li>
<li> CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning</li>
<li> HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</li>
<li> AG: Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow</li>
</ul>
Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="thursday-august-26">Thursday August 26 </h2>
<p>
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a>.
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from Fall Semester 2020</a>.
<p>
The lectures will be recorded and updated videos will be posted after the lectures.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -285,7 +312,7 @@ MathJax.Hub.Config({
<ul>
<li> <b>Phone</b>: +47-48257387</li>
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li>
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
<li> <b>Office hours</b>: <em>Anytime</em>! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.</li>
</ul>
<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
@@ -339,10 +366,10 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<li> The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html." target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.</tt></a></li>
</ol>
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.
<ol>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a> This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.</li>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
@@ -853,14 +880,14 @@ $$
<p>
<table border="1">
<thead>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
</thead>
<tbody>
<tr><td align="center"> \( A = A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij} = a_{ji} \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A = A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij} = a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij} = a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij}=a_{ji} \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij}=a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij}=a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) </td> </tr>
</tbody>
</table>
@@ -1309,7 +1336,7 @@ But before we really start with nuclear physics data, let's just look at some si
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="friday-august-21">Friday August 21 </h2>
<h2 id="friday-august-27">Friday August 27 </h2>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week34/html/week34.html
+40 -13
View File
@@ -67,6 +67,7 @@ div { text-align: justify; text-justify: inter-word; }
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of first week', 2, None, 'overview-of-first-week'),
('Reading Recommendations', 2, None, 'reading-recommendations'),
('Thursday August 26', 2, None, 'thursday-august-26'),
('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
('Course Format', 2, None, 'course-format'),
@@ -134,7 +135,7 @@ div { text-align: justify; text-justify: inter-word; }
('Meet the Pandas', 2, None, 'meet-the-pandas'),
('Friday August 21', 2, None, 'friday-august-21'),
('Reading Data and fitting', 2, None, 'reading-data-and-fitting'),
('Friday August 21', 2, None, 'friday-august-21'),
('Friday August 27', 2, None, 'friday-august-27'),
('Simple linear regression model using _scikit-learn_',
3,
None,
@@ -195,7 +196,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Aug 8, 2021</h4></center> <!-- date -->
<center><h4>Aug 23, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -208,6 +209,7 @@ MathJax.Hub.Config({
<p>
<ul>
<li> Wednesday August 25: Introduction to software and repetition of Python Programming</li>
<li> Thursday August 26: First lecture: Presentation of the course, aims and content</li>
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
<li> Friday August 27: Linear regression</li>
@@ -216,13 +218,38 @@ MathJax.Hub.Config({
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="reading-recommendations">Reading Recommendations </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
For the reading assignments we use the following abbreviations:
<ul>
<li> GBC: Goodfellow, Bengio, and Courville, Deep Learning</li>
<li> CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning</li>
<li> HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</li>
<li> AG: Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow</li>
</ul>
Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="thursday-august-26">Thursday August 26 </h2>
<p>
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a>.
<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from Fall Semester 2020</a>.
<p>
The lectures will be recorded and updated videos will be posted after the lectures.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -290,7 +317,7 @@ MathJax.Hub.Config({
<ul>
<li> <b>Phone</b>: +47-48257387</li>
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li>
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
<li> <b>Office hours</b>: <em>Anytime</em>! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.</li>
</ul>
<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
@@ -344,10 +371,10 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<li> The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html." target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.</tt></a></li>
</ol>
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.
<ol>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a> This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.</li>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
@@ -858,14 +885,14 @@ $$
<p>
<table border="1">
<thead>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
<tr><th align="center"> Relations </th> <th align="center"> Name </th> <th align="center"> matrix elements </th> </tr>
</thead>
<tbody>
<tr><td align="center"> \( A = A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij} = a_{ji} \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A = A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij} = a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij} = a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A = \left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{T} \) </td> <td align="center"> symmetric </td> <td align="center"> \( a_{ij}=a_{ji} \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{T} \right )^{-1} \) </td> <td align="center"> real orthogonal </td> <td align="center"> \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj} = \delta_{ij} \) </td> </tr>
<tr><td align="center"> \( A=A^{ * } \) </td> <td align="center"> real matrix </td> <td align="center"> \( a_{ij}=a_{ij}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=A^{\dagger} \) </td> <td align="center"> hermitian </td> <td align="center"> \( a_{ij}=a_{ji}^{ * } \) </td> </tr>
<tr><td align="center"> \( A=\left (A^{\dagger} \right )^{-1} \) </td> <td align="center"> unitary </td> <td align="center"> \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) </td> </tr>
</tbody>
</table>
@@ -1314,7 +1341,7 @@ But before we really start with nuclear physics data, let's just look at some si
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="friday-august-21">Friday August 21 </h2>
<h2 id="friday-august-27">Friday August 27 </h2>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
BIN
View File
Binary file not shown.
doc/pub/week34/ipynb/week34.ipynb
+34 -12
View File
@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Aug 8, 2021**\n",
"Date: **Aug 23, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -18,8 +18,13 @@
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Overview of first week\n",
"\n",
" * Wednesday August 25: Introduction to software and repetition of Python Programming\n",
"\n",
" * Thursday August 26: First lecture: Presentation of the course, aims and content\n",
"\n",
" * Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics\n",
@@ -30,11 +35,28 @@
"\n",
"\n",
"\n",
"## Reading Recommendations\n",
"\n",
"For the reading assignments we use the following abbreviations:\n",
"* GBC: Goodfellow, Bengio, and Courville, Deep Learning\n",
"\n",
"* CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning\n",
"\n",
"* HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning\n",
"\n",
"* AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow\n",
"\n",
"Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at <https://compphysics.github.io/MachineLearning/doc/web/course.html>\n",
"\n",
"\n",
"\n",
"\n",
"## Thursday August 26\n",
"\n",
"[Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage).\n",
"\n",
"[Video of Lecture from Fall Semester 2020](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage).\n",
"\n",
"The lectures will be recorded and updated videos will be posted after the lectures. \n",
"\n",
"## Lectures and ComputerLab\n",
"\n",
@@ -87,7 +109,7 @@
"\n",
" * **Office**: Department of Physics, University of Oslo, Eastern wing, room FØ470 \n",
"\n",
" * **Office hours**: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.\n",
" * **Office hours**: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning. \n",
"\n",
"\n",
"* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no\n",
@@ -129,9 +151,9 @@
"\n",
"1. The lecture notes are collected as a jupyter-book at <https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.>\n",
"\n",
"In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.\n",
"In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.\n",
"\n",
"1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <https://www.springer.com/gp/book/9780387310732.> This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.\n",
"1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <https://www.springer.com/gp/book/9780387310732.> \n",
"\n",
"2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <https://www.deeplearningbook.org/.> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.\n",
"\n",
@@ -616,14 +638,14 @@
"source": [
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\"> Relations </th> <th align=\"center\"> Name </th> <th align=\"center\"> matrix elements </th> </tr>\n",
"<tr><th align=\"center\"> Relations </th> <th align=\"center\"> Name </th> <th align=\"center\"> matrix elements </th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"center\"> $A = A^{T}$ </td> <td align=\"center\"> symmetric </td> <td align=\"center\"> $a_{ij} = a_{ji}$ </td> </tr>\n",
"<tr><td align=\"center\"> $A = \\left (A^{T} \\right )^{-1}$ </td> <td align=\"center\"> real orthogonal </td> <td align=\"center\"> $\\sum_k a_{ik} a_{jk} = \\sum_k a_{ki} a_{kj} = \\delta_{ij}$ </td> </tr>\n",
"<tr><td align=\"center\"> $A = A^{ * }$ </td> <td align=\"center\"> real matrix </td> <td align=\"center\"> $a_{ij} = a_{ij}^{ * }$ </td> </tr>\n",
"<tr><td align=\"center\"> $A = A^{\\dagger}$ </td> <td align=\"center\"> hermitian </td> <td align=\"center\"> $a_{ij} = a_{ji}^{ * }$ </td> </tr>\n",
"<tr><td align=\"center\"> $A = \\left (A^{\\dagger} \\right )^{-1}$ </td> <td align=\"center\"> unitary </td> <td align=\"center\"> $\\sum_k a_{ik} a_{jk}^{ * } = \\sum_k a_{ki}^{ * } a_{kj} = \\delta_{ij}$ </td> </tr>\n",
"<tr><td align=\"center\"> $A=A^{T}$ </td> <td align=\"center\"> symmetric </td> <td align=\"center\"> $a_{ij}=a_{ji}$ </td> </tr>\n",
"<tr><td align=\"center\"> $A=\\left (A^{T} \\right )^{-1}$ </td> <td align=\"center\"> real orthogonal </td> <td align=\"center\"> $\\sum_k a_{ik}a_{jk}=\\sum_k a_{ki} a_{kj} = \\delta_{ij}$ </td> </tr>\n",
"<tr><td align=\"center\"> $A=A^{ * }$ </td> <td align=\"center\"> real matrix </td> <td align=\"center\"> $a_{ij}=a_{ij}^{ * }$ </td> </tr>\n",
"<tr><td align=\"center\"> $A=A^{\\dagger}$ </td> <td align=\"center\"> hermitian </td> <td align=\"center\"> $a_{ij}=a_{ji}^{ * }$ </td> </tr>\n",
"<tr><td align=\"center\"> $A=\\left (A^{\\dagger} \\right )^{-1}$ </td> <td align=\"center\"> unitary </td> <td align=\"center\"> $\\sum_k a_{ik}a_{jk}^{ * }=\\sum_k a_{ki}^{ * } a_{kj}=\\delta_{ij}$ </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"\n",
@@ -1354,7 +1376,7 @@
"But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as,\n",
"(don't be offended) fitting straight lines!\n",
"\n",
"## Friday August 21\n",
"## Friday August 27\n",
"\n",
"### Simple linear regression model using **scikit-learn**\n",
"\n",
doc/src/week34/week34.do.txt
+29 -10
View File
@@ -4,22 +4,41 @@ DATE: today
!split
===== Overview of first week =====
!bblock
* Wednesday August 25: Introduction to software and repetition of Python Programming
* Thursday August 26: First lecture: Presentation of the course, aims and content
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics
* Friday August 27: Linear regression
* Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 25.
!eblock
!split
===== Reading Recommendations =====
!bblock
For the reading assignments we use the following abbreviations:
* GBC: Goodfellow, Bengio, and Courville, Deep Learning
* CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
* HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
* AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
!eblock
!split
===== Thursday August 26 =====
"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage".
"Video of Lecture from Fall Semester 2020":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage".
The lectures will be recorded and updated videos will be posted after the lectures.
!split
===== Lectures and ComputerLab =====
@@ -61,7 +80,7 @@ _Teachers :_
* Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
* _Phone_: +47-48257387
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
* _Office hours_: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.
* _Office hours_: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ452
@@ -94,9 +113,9 @@ Projects are handed in using _Canvas_. We use Github as repository for codes, be
o The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.
o Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.
o Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.
o Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
@@ -535,11 +554,11 @@ The inverse of a matrix is defined by
|----------------------------------------------------------------------|
| Relations | Name | matrix elements |
|----------------------------------------------------------------------|
| $A = A^{T}$ | symmetric | $a_{ij} = a_{ji}$ |
| $A = \left (A^{T} \right )^{-1}$ | real orthogonal | $\sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij}$ |
| $A = A^{ * }$ | real matrix | $a_{ij} = a_{ij}^{ * }$ |
| $A = A^{\dagger}$ | hermitian | $a_{ij} = a_{ji}^{ * }$ |
| $A = \left (A^{\dagger} \right )^{-1}$ | unitary | $\sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij}$ |
| $A=A^{T}$ | symmetric | $a_{ij}=a_{ji}$ |
| $A=\left (A^{T} \right )^{-1}$ | real orthogonal | $\sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj} = \delta_{ij}$ |
| $A=A^{ * }$ | real matrix | $a_{ij}=a_{ij}^{ * }$ |
| $A=A^{\dagger}$ | hermitian | $a_{ij}=a_{ji}^{ * }$ |
| $A=\left (A^{\dagger} \right )^{-1}$ | unitary | $\sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij}$ |
|----------------------------------------------------------------------|
!eblock
@@ -906,7 +925,7 @@ But before we really start with nuclear physics data, let's just look at some si
(don't be offended) fitting straight lines!
!split
===== Friday August 21 =====
===== Friday August 27 =====
!split
=== Simple linear regression model using _scikit-learn_ ===