update first week

Author: mhjensen

doc/pub/week1/html/week1-bs.html

@@ -212,9 +212,9 @@ <h2 id="practicalities">Practicalities  </h2>
 
 <ol>
 <p><li> Lectures Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li>
-<p><li> Lab and exercise sessions Thursdays 215pm-4pm, , room F&#216;434, Department of Physics</li> 
+<p><li> Lab and exercise sessions Thursdays 215pm-4pm, room F&#216;434, Department of Physics</li> 
 <p><li> We plan to work on two projects which will define the content of the course, the format can be agreed upon by the participants</li>
-<p><li> No exam, only two projects. Each projects counts 1/2 of the final grade. Aleternatively one long project.</li>
+<p><li> No exam, only two projects. Each projects counts 1/2 of the final grade. Aleternatively, one long project which counts 100% of the final grade</li>
 <p><li> All info at the GitHub address <a href="https://github.com/CompPhysics/AdvancedMachineLearning" target="_blank"><tt>https://github.com/CompPhysics/AdvancedMachineLearning</tt></a></li>
 </ol>
 </section>
@@ -223,7 +223,7 @@ <h2 id="practicalities">Practicalities  </h2>
 <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered, tentative </h2>
 
 <ol>
-<p><li> <b>Deep learning, classics</b>
+<p><li> <b>Deep learning</b>
 <ol type="a"></li>
  <p><li> Feed forward neural networks and its mathematics (NNs)</li>
  <p><li> Convolutional neural networks (CNNs)</li>
@@ -244,7 +244,7 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <p><li> Autoregressive methods (tentative)</li>
 </ol>
 <p>
-<p><li> <b>Physical Sciences (often just called Physics informed) informed machine learning</b></li>
+<p><li> <b>Physical Sciences (often just called Physics informed neural networks, PINNs) informed machine learning</b></li>
 </ol>
 </section>
 
@@ -258,28 +258,11 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
-<p>We have not made plans for Reinforcement learning, but this can be another option.</p>
+<p>We have not made plans for Reinforcement learning.</p>
 </section>
 
 <section>
-<h2 id="good-books-with-hands-on-material-and-codes">Good books with hands-on material and codes </h2>
-<div class="alert alert-block alert-block alert-text-normal">
-<b></b>
-<p>
-<ul>
-<p><li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
-<p><li> <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html" target="_blank">David Foster, Generative Deep Learning with TensorFlow</a></li>
-<p><li> <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_blank">Bali and Gavras, Generative AI with Python and TensorFlow 2</a></li>
-</ul>
-</div>
-
-<p>All three books have GitHub addresses from where  one can download all codes. We will borrow most of the material from these three texts as well as 
-from Goodfellow, Bengio and Courville's text <a href="https://www.deeplearningbook.org/" target="_blank">Deep Learning</a>
-</p>
-</section>
-
-<section>
-<h2 id="project-paths">Project paths </h2>
+<h2 id="project-paths-overarching-view">Project paths, overarching view </h2>
 
 <p>The course can also be used as a self-study course and besides the
 lectures, many of you may wish to independently work on your own
@@ -296,6 +279,101 @@ <h2 id="project-paths">Project paths </h2>
 </ol>
 </section>
 
+<section>
+<h2 id="possible-paths-for-the-projects">Possible paths for the projects </h2>
+
+<p>The differential equation path: Here we propose a set of differential
+equations (ordinary and/or partial) to be solved first using neural
+networks (using either your own code or TensorFlow/Pytorch or similar
+libraries). Thereafter we can extend the set of methods for
+solving these equations to recurrent neural networks and autoencoders
+(AE) and/or Generalized Adversarial Networks (GANs). All these
+approaches can be expanded into one large project. This project can
+also be extended into including <a href="https://github.com/maziarraissi/PINNs" target="_blank">Physics informed machine
+learning</a>. Here we can discuss
+neural networks that are trained to solve supervised learning tasks
+while respecting any given law of physics described by general
+nonlinear partial differential equations.
+</p>
+
+<p>For those interested in mathematical aspects of deep learning, this could also be included.</p>
+</section>
+
+<section>
+<h2 id="the-generative-models">The generative models </h2>
+
+<p>This path brings us from discriminative models (like the standard application of NNs, CNNs etc) to generative models. Two projects that follow to a large extent
+the lectures. Topics for data sets will be discussed.
+</p>
+</section>
+
+<section>
+<h2 id="paths-for-projects-writing-own-codes">Paths for projects, writing own codes </h2>
+
+<p>The computational path: Here we propose a path where you develop your
+own code for a convolutional or eventually recurrent neural network
+and apply this to data selects of your own selection. The code should
+be object oriented and flexible allowing for eventual extensions by
+including different Loss/Cost functions and other
+functionalities. Feel free to select data sets from those suggested
+below here. This code can also be extended upon by adding for example
+autoencoders. You can compare your own codes with implementations
+using TensorFlow(Keras)/PyTorch or other libraries.
+</p>
+</section>
+
+<section>
+<h2 id="the-application-path-own-data">The application path/own data </h2>
+
+<p>The application path: Here you can use the most relevant method(s)
+(say convolutional neural networks for images) and apply this(these)
+to data sets relevant for your own research.
+</p>
+</section>
+
+<section>
+<h2 id="gaussian-processes-and-bayesian-analysis">Gaussian processes and Bayesian analysis </h2>
+
+<p>The Gaussian processes/Bayesian statistics path: <a href="https://jenfb.github.io/bkmr/overview.html" target="_blank">Kernel regression
+(Gaussian processes) and Bayesian
+statistics</a> are popular
+tools in the machine learning literature. The main idea behind these
+approaches is to flexibly model the relationship between a large
+number of variables and a particular outcome (dependent
+variable). This can form a second part of a project where for example
+standard Kernel regression methods are used on a specific data
+set. Alternatively, participants can opt to work on a large project
+relevant for their own research using gaussian processes and/or
+Bayesian machine Learning.
+</p>
+</section>
+
+<section>
+<h2 id="hpc-path">HPC path </h2>
+
+<p>Another alternative is to study high-performance computing aspects in
+designing ML codes. This can also be linked with an exploration of
+mathematical aspects of deep learning methods.
+</p>
+</section>
+
+<section>
+<h2 id="good-books-with-hands-on-material-and-codes">Good books with hands-on material and codes </h2>
+<div class="alert alert-block alert-block alert-text-normal">
+<b></b>
+<p>
+<ul>
+<p><li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
+<p><li> <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html" target="_blank">David Foster, Generative Deep Learning with TensorFlow</a></li>
+<p><li> <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_blank">Bali and Gavras, Generative AI with Python and TensorFlow 2</a></li>
+</ul>
+</div>
+
+<p>All three books have GitHub addresses from where  one can download all codes. We will borrow most of the material from these three texts as well as 
+from Goodfellow, Bengio and Courville's text <a href="https://www.deeplearningbook.org/" target="_blank">Deep Learning</a>
+</p>
+</section>
+
 <section>
 <h2 id="types-of-machine-learning">Types of machine learning </h2>
 
@@ -363,7 +441,7 @@ <h2 id="what-is-generative-modeling">What Is Generative Modeling? </h2>
 </section>
 
 <section>
-<h2 id="example-of-generative-modeling-taken-from-generative-deeep-learning-by-david-foster-https-www-oreilly-com-library-view-generative-deep-learning-9781098134174-ch01-html">Example of generative modeling, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html" target="_blank">taken from Generative Deeep Learning by David Foster</a>  </h2>
+<h2 id="example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster-https-www-oreilly-com-library-view-generative-deep-learning-9781098134174-ch01-html">Example of generative modeling, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html" target="_blank">taken from Generative Deep Learning by David Foster</a>  </h2>
 
 <br/><br/>
 <center>
@@ -442,85 +520,7 @@ <h2 id="taxonomy-of-generative-deep-learning-taken-from-generative-deep-learning
 </section>
 
 <section>
-<h2 id="possible-paths-for-the-projects">Possible paths for the projects </h2>
-
-<p>The differential equation path: Here we propose a set of differential
-equations (ordinary and/or partial) to be solved first using neural
-networks (using either your own code or TensorFlow/Pytorch or similar
-libraries). Thereafter we can extend the set of methods for
-solving these equations to recurrent neural networks and autoencoders
-(AE) and/or Generalized Adversarial Networks (GANs). All these
-approaches can be expanded into one large project. This project can
-also be extended into including <a href="https://github.com/maziarraissi/PINNs" target="_blank">Physics informed machine
-learning</a>. Here we can discuss
-neural networks that are trained to solve supervised learning tasks
-while respecting any given law of physics described by general
-nonlinear partial differential equations.
-</p>
-
-<p>For those interested in mathematical aspects of deep learning, this could also be included.</p>
-</section>
-
-<section>
-<h2 id="the-generative-models">The generative models </h2>
-
-<p>This path brings us from discriminative models (like the standard application of NNs, CNNs etc) to generative models. Two projects that follow to a large extent
-the lectures. Topics for data sets will be discussed during the lab sessions.
-</p>
-</section>
-
-<section>
-<h2 id="paths-for-projects-writing-own-codes">Paths for projects, writing own codes </h2>
-
-<p>The computational path: Here we propose a path where you develop your
-own code for a convolutional or eventually recurrent neural network
-and apply this to data selects of your own selection. The code should
-be object oriented and flexible allowing for eventual extensions by
-including different Loss/Cost functions and other
-functionalities. Feel free to select data sets from those suggested
-below here. This code can also be extended upon by adding for example
-autoencoders. You can compare your own codes with implementations
-using TensorFlow(Keras)/PyTorch or other libraries.
-</p>
-</section>
-
-<section>
-<h2 id="the-application-path">The application path </h2>
-
-<p>The application path: Here you can use the most relevant method(s)
-(say convolutional neural networks for images) and apply this(these)
-to data sets relevant for your own research.
-</p>
-</section>
-
-<section>
-<h2 id="gaussian-processes-and-bayesian-analysis">Gaussian processes and Bayesian analysis </h2>
-
-<p>The Gaussian processes/Bayesian statistics path: <a href="https://jenfb.github.io/bkmr/overview.html" target="_blank">Kernel regression
-(Gaussian processes) and Bayesian
-statistics</a> are popular
-tools in the machine learning literature. The main idea behind these
-approaches is to flexibly model the relationship between a large
-number of variables and a particular outcome (dependent
-variable). This can form a second part of a project where for example
-standard Kernel regression methods are used on a specific data
-set. Alternatively, participants can opt to work on a large project
-relevant for their own research using gaussian processes and/or
-Bayesian machine Learning.
-</p>
-</section>
-
-<section>
-<h2 id="hpc-path">HPC path </h2>
-
-<p>Another alternative is to study high-performance computing aspects in
-designing ML codes. This can also be linked with an exploration of
-mathematical aspects of deep learning methods.
-</p>
-</section>
-
-<section>
-<h2 id="what-are-the-basic-machine-learning-ingredients">What are the basic Machine Learning ingredients? </h2>
+<h2 id="reminder-on-the-basic-machine-learning-ingredients">Reminder on  the basic Machine Learning ingredients </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>