updated aims for week 39
This commit is contained in:
Morten Hjorth-Jensen
@@ -197,20 +197,53 @@ MathJax.Hub.Config({
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<section>
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<h2 id="plan-for-week-39">Plan for week 39 </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the active learning sessions on Tuesday and Wednesday</b>
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<p>
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<ul>
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<p><li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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<p><li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<p><li> Reading recommendations:</li>
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<p><li> Discussions on how to structure your report for the first project</li>
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<p><li> Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.</li>
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<p><li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
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</ul>
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<p>
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<p>See <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank">lecture notes for week 39</a>.
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For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
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</p>
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<p>For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.</p>
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<b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.
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<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
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<ul>
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<p><li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
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</ul>
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</div>
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the lecture on Thursday September 28</b>
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<p>
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<ul>
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<p><li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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<p><li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<p><li> <a href="https://youtu.be/" target="_blank">Video of lecture</a></li>
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<p><li> Whiteboard notes TBA at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf</tt></a></li>
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<p><li> Readings and Videos:</li>
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<ul>
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<p><li> These lecture notes</li>
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<p><li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<p><li> <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank">Video on gradient descent</a></li>
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<p><li> <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank">Video on stochastic gradient descent</a></li>
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</ul>
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<p>
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</ul>
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</div>
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</section>
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<section>
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@@ -342,19 +342,39 @@ MathJax.Hub.Config({
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="plan-for-week-39">Plan for week 39 </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the active learning sessions on Tuesday and Wednesday</b>
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<p>
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<ul>
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<li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> Reading recommendations:</li>
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<li> Discussions on how to structure your report for the first project</li>
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<li> Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.</li>
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<li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
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</ul>
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<p>See <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank">lecture notes for week 39</a>.
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For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
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</p>
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<p>For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.</p>
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<b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.
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<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
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<ul>
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<li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
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</ul>
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</div>
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the lecture on Thursday September 28</b>
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<p>
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<ul>
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<li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> <a href="https://youtu.be/" target="_blank">Video of lecture</a></li>
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<li> Whiteboard notes TBA at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf</tt></a></li>
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<li> Readings and Videos:</li>
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<ul>
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<li> These lecture notes</li>
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<li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<li> <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank">Video on gradient descent</a></li>
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<li> <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank">Video on stochastic gradient descent</a></li>
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</ul>
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</ul>
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</div>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="optimization-the-central-part-of-any-machine-learning-algortithm">Optimization, the central part of any Machine Learning algortithm </h2>
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@@ -419,19 +419,39 @@ MathJax.Hub.Config({
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="plan-for-week-39">Plan for week 39 </h2>
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||||
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the active learning sessions on Tuesday and Wednesday</b>
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||||
<p>
|
||||
<ul>
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<li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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||||
<li> Stochastic Gradient descent with examples and automatic differentiation</li>
|
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<li> Reading recommendations:</li>
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<li> Discussions on how to structure your report for the first project</li>
|
||||
<li> Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.</li>
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<li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
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</ul>
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<p>See <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank">lecture notes for week 39</a>.
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For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
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</p>
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<p>For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.</p>
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||||
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<b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.
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<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
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||||
<ul>
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<li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
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</ul>
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</div>
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<div class="alert alert-block alert-block alert-text-normal">
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<b>Material for the lecture on Thursday September 28</b>
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<p>
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<ul>
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<li> Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</li>
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||||
<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> <a href="https://youtu.be/" target="_blank">Video of lecture</a></li>
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<li> Whiteboard notes TBA at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf</tt></a></li>
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<li> Readings and Videos:</li>
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<ul>
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<li> These lecture notes</li>
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<li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<li> <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank">Video on gradient descent</a></li>
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<li> <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank">Video on stochastic gradient descent</a></li>
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</ul>
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</ul>
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</div>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="optimization-the-central-part-of-any-machine-learning-algortithm">Optimization, the central part of any Machine Learning algortithm </h2>
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@@ -1,59 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f35930ac",
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"metadata": {
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"editable": true
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},
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"source": [
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"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
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"doconce format html exercisesweek39.do.txt -->\n",
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"<!-- dom:TITLE: Exercises week 39 -->"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8cb567a0",
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"metadata": {
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"editable": true
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},
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"source": [
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"# Exercises week 39\n",
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"**September 25-29, 2023**\n",
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"\n",
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"Date: **Deadline is Sunday October 1 at midnight**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "934324b3",
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"metadata": {
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"editable": true
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},
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"source": [
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"## Overarching aims of the exercises this week\n",
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"\n",
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"The aim of the exercises this week is to aid you in getting started\n",
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"with writing the report. This will be discussed during the lab\n",
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"sessions as well. One of the lab sessions will be recorded.\n",
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"\n",
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"A general guideline can be found at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md>.\n",
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"\n",
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"Similarly, an example of an earlier project can be found at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf>\n",
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"\n",
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"Your task this week is to\n",
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"1. Write an abstract for your project\n",
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"\n",
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"2. Write an introduction\n",
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"\n",
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"3. Include references\n",
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"\n",
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"Ashort feedback to the this exercise will be available after the deadline. And you can reuse these elements in your final report."
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -5,18 +5,30 @@ DATE: Week 39
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!split
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===== Plan for week 39 =====
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* Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent
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* Stochastic Gradient descent with examples and automatic differentiation
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|
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* Reading recommendations:
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See "lecture notes for week 39":"https://compphysics.github.io/MachineLearning/doc/web/course.html".
|
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For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
|
||||
|
||||
For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.
|
||||
|
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_For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11_.
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!bblock Material for the active learning sessions on Tuesday and Wednesday
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* Discussions on how to structure your report for the first project
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||||
* Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.
|
||||
* Work on project 1, in particular resampling methods like cross-validation and bootstrap. _For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11_.
|
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These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning.
|
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* A general guideline can be found at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md".
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!eblock
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!bblock Material for the lecture on Thursday September 28
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* Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent
|
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* Stochastic Gradient descent with examples and automatic differentiation
|
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* "Video of lecture":"https://youtu.be/"
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* Whiteboard notes TBA at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep28.pdf"
|
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* Readings and Videos:
|
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* These lecture notes
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* For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.
|
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* "Video on gradient descent":"https://www.youtube.com/watch?v=sDv4f4s2SB8"
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* "Video on stochastic gradient descent":"https://www.youtube.com/watch?v=vMh0zPT0tLI"
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!eblock
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@@ -2687,3 +2699,6 @@ print(derivative_fn(x_small))
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!ec
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