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Morten Hjorth-Jensen
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doc/pub/week34/html/._week34-bs003.html
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<li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.</li>
<li> Thursdays: regular lectures (12.15pm-2pm)</li>
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
<li> Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
<li> Weekly plans and all other information are on the official website. This info will also be conveyed via weekly emails.</li>
<li> No final exam, three projects that are graded and have to be approved.</li>
doc/pub/week34/html/._week34-bs005.html
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<li> If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course</li>
<li> Based on feedback etc, each group finalizes the report and submits for grading.</li>
</ol>
<li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_self">github address</a> of the course.</li>
<li> Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_self">github address</a> of the course.</li>
</ul>
<p>
<!-- navigation buttons at the bottom of the page -->
doc/pub/week34/html/._week34-bs008.html
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<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</tt></a>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide for links.</p>
</div>
</div>
doc/pub/week34/html/._week34-bs009.html
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<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_self">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_self"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_self"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_self"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.* Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_self"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_self">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
</ul>
</div>
doc/pub/week34/html/week34-reveal.html
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<p><li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
<p><li> Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.</li>
<p><li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
<p><li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
@@ -283,7 +283,7 @@ MathJax.Hub.Config({
</ol>
<p>
<p><li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
<p><li> Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
</ul>
</section>
@@ -334,7 +334,7 @@ MathJax.Hub.Config({
<p>
<p>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>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide for links.</p>
</div>
</section>
@@ -347,8 +347,7 @@ MathJax.Hub.Config({
<ul>
<p><li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<p><li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<p><li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>. This is a well-known text and serves as additional literature.</li>
<p><li> Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<p><li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.* Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<p><li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
</ul>
</div>
doc/pub/week34/html/week34-solarized.html
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@@ -344,7 +344,7 @@ MathJax.Hub.Config({
<li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.</li>
<li> Thursdays: regular lectures (12.15pm-2pm)</li>
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
<li> Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
<li> Weekly plans and all other information are on the official website. This info will also be conveyed via weekly emails.</li>
<li> No final exam, three projects that are graded and have to be approved.</li>
@@ -370,7 +370,7 @@ MathJax.Hub.Config({
<li> If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course</li>
<li> Based on feedback etc, each group finalizes the report and submits for grading.</li>
</ol>
<li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
<li> Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="teachers">Teachers </h2>
@@ -412,7 +412,7 @@ MathJax.Hub.Config({
<p>
<p>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>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide for links.</p>
</div>
@@ -425,8 +425,7 @@ MathJax.Hub.Config({
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.* Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
</ul>
</div>
doc/pub/week34/html/week34.html
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@@ -421,7 +421,7 @@ MathJax.Hub.Config({
<li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.</li>
<li> Thursdays: regular lectures (12.15pm-2pm)</li>
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
<li> Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
<li> Weekly plans and all other information are on the official website. This info will also be conveyed via weekly emails.</li>
<li> No final exam, three projects that are graded and have to be approved.</li>
@@ -447,7 +447,7 @@ MathJax.Hub.Config({
<li> If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course</li>
<li> Based on feedback etc, each group finalizes the report and submits for grading.</li>
</ol>
<li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
<li> Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="teachers">Teachers </h2>
@@ -489,7 +489,7 @@ MathJax.Hub.Config({
<p>
<p>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>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide for links.</p>
</div>
@@ -502,8 +502,7 @@ MathJax.Hub.Config({
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.* Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
</ul>
</div>
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doc/src/week34/week34.do.txt
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@@ -40,7 +40,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
* The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.
* Thursdays: regular lectures (12.15pm-2pm)
* Weekly reading assignments and videos needed to solve projects and exercises.
* Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.
* Weekly exercises. You can hand in exercises if you want and get an extra score, see below.
* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
* Weekly plans and all other information are on the official website. This info will also be conveyed via weekly emails.
* No final exam, three projects that are graded and have to be approved.
@@ -62,7 +62,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
o For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project.
o If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course
o Based on feedback etc, each group finalizes the report and submits for grading.
* Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
* Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
@@ -106,17 +106,17 @@ Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Fri
!bblock
The lecture notes are collected as a jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html".
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide.
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these 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 next slide for links.
!eblock
!split
===== Textbooks =====
!bblock
* "Goodfellow, Bengio, and Courville (GBC), Deep Learning":"https://www.deeplearningbook.org/"
* Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
* Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570". This is a well-known text and serves as additional literature.
* Aurelien Geron (AG), Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
* Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570".* Aurelien Geron (AG), Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
* "Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction":"https://probml.github.io/pml-book/book1.html"
!eblock