updating schedule
This commit is contained in:
Morten Hjorth-Jensen
@@ -481,7 +481,9 @@
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 38 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>Chapter</p></li>
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<li><p>Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression</p></li>
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<li><p>Hastie et al 4.1, 4.2 and 4.3 on logistic regression</p></li>
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<li><p>For a good discussion on gradient methods, see 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.</p></li>
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</ul>
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</li>
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</ul>
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@@ -491,10 +493,12 @@
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<ul class="simple">
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<li><p>Lab Wednesday: Work on Project 1</p></li>
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<li><p>Lecture Thursday: Gradient Optimization methods</p></li>
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<li><p>Lecture Friday: Deep Learning and Neural Networks</p></li>
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<li><p>Lecture Friday: Gradient methods and Deep Learning and Neural Networks</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 39 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>For a good discussion on gradient methods, see 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.</p></li>
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<li><p>For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4</p></li>
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</ul>
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</li>
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</ul>
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@@ -505,18 +509,24 @@
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<li><p>Lab Wednesday: Wrap up project 1 and start project 2</p></li>
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<li><p>Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification</p></li>
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<li><p>Lecture Friday: Deep Learning and TensorFlow and Keras</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 40 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-41-october-11-15">
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<h3>Week 41 October 11-15<a class="headerlink" href="#week-41-october-11-15" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 2</p></li>
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<li><p>Lecture Thursday: Deep learning and Neural Networks</p></li>
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<li><p>Lecture Friday: Convolutional Neural Networks, basic elements</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>GoodFellow et al, Chapter 9</p></li>
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<li><p>See lecture notes for week 41 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications</p></li>
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</ul>
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</li>
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</ul>
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@@ -524,64 +534,97 @@
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<div class="section" id="week-42-october-18-22">
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<h3>Week 42 October 18-22<a class="headerlink" href="#week-42-october-18-22" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 2</p></li>
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<li><p>Lecture Thursday: Convolutional Neural Networks and classification problems</p></li>
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<li><p>Lecture Friday: Convolutional Neural Networks and classification problems</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 42 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-43-october-25-29">
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<h3>Week 43 October 25-29<a class="headerlink" href="#week-43-october-25-29" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 2</p></li>
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<li><p>Lecture Thursday: Recurrent Neural Networks</p></li>
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<li><p>Lecture Friday: Recurrent Neural Networks and time series</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 43 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-44-november-1-5">
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<h3>Week 44 November 1-5<a class="headerlink" href="#week-44-november-1-5" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 2</p></li>
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<li><p>Lecture Thursday: Decision trees, classification and regression</p></li>
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<li><p>Lecture Friday: Decision trees, basic algorithms</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 44 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>Hastie et al sections 9.1 and 9.2</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-45-november-8-12">
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<h3>Week 45 November 8-12<a class="headerlink" href="#week-45-november-8-12" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 3</p></li>
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<li><p>Lecture Thursday: Ensemble methods, bagging and random forests</p></li>
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<li><p>Lecture Friday: Boosting and gradient boosting</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 45 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>Hastie et al chapter 10</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-46-november-15-19">
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<h3>Week 46 November 15-19<a class="headerlink" href="#week-46-november-15-19" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lecture Thursday:</p></li>
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<li><p>Lecture Friday: Unsupervised Learning, k-means</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Lab Wednesday: Work on project 3</p></li>
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<li><p>Lecture Thursday: Support Vector machines</p></li>
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<li><p>Lecture Friday: Support Vector machines</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 46 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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<li><p>Hastie et al chapter 12</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-47-november-22-26">
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<h3>Week 47 November 22-26<a class="headerlink" href="#week-47-november-22-26" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 3</p></li>
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<li><p>Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)</p></li>
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<li><p>Lecture Friday: Unsupervised Learning and PCA and Clustering</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Lecture Friday: Unsupervised Learning and PCA, k-means and Clustering</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 47 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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</ul>
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</li>
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</ul>
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</div>
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<div class="section" id="week-48-november-29-december-2">
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<h3>Week 48 November 29- December 2<a class="headerlink" href="#week-48-november-29-december-2" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Lab Wednesday:</p></li>
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<li><p>Lab Wednesday: Work on project 3</p></li>
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<li><p>Lecture Thursday: Unsupervised Learning and Clustering</p></li>
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<li><p>Lecture Friday: Summary of course</p></li>
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<li><p>Reading recommendations:</p></li>
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<li><p>Reading recommendations:</p>
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<ul>
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<li><p>See lecture notes for week 48 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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