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Morten Hjorth-Jensen
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doc/LectureNotes/_build/.doctrees/environment.pickle
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@@ -1117,6 +1117,7 @@ doconce format html week35.do.txt --no_mako -->
<h3>Reading recommendations:<a class="headerlink" href="#reading-recommendations" title="Permalink to this headline"></a></h3>
<ol class="simple">
<li><p>These lecture notes</p></li>
<li><p><a class="reference external" href="https://youtu.be/VKakN-e4aUA">Video of lecture</a></p></li>
<li><p>Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</p></li>
<li><p>Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</p></li>
<li><p>For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL”<a class="reference external" href="https://mml-book.github.io/">https://mml-book.github.io/</a>” (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</p></li>
@@ -1592,7 +1593,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9935551267466322
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.995597266739957
</pre></div>
</div>
</div>
@@ -1609,7 +1610,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.010490200097725966
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.007984802498580442
</pre></div>
</div>
</div>
@@ -1624,23 +1625,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.0154132 0.05341376 0.03110433 0.00569304 0.00506878 0.0148722
0.08128265 0.02279801 0.02093685 0.06532712 0.04539538 0.03257064
0.02869709 0.08842405 0.05211842 0.07932386 0.02115366 0.04175792
0.01811045 0.06077967 0.04565093 0.03645765 0.06066397 0.00485256
0.01797226 0.02614999 0.01806634 0.02108223 0.0066334 0.05980771
0.04400708 0.00630419 0.1383885 0.00044903 0.00641337 0.03058676
0.00976807 0.02733896 0.05637138 0.02881541 0.0163562 0.03518305
0.02460355 0.00280363 0.0044697 0.01549726 0.01692802 0.01292378
0.03745017 0.02810512 0.02286429 0.02165757 0.02666129 0.00779268
0.03131926 0.02339588 0.00461151 0.01516054 0.04771954 0.04964381
0.00310577 0.07038555 0.02244542 0.00549812 0.03432913 0.02357982
0.03663294 0.02518542 0.04143717 0.03388678 0.00780776 0.00132783
0.02747654 0.04561434 0.00158754 0.02174822 0.03353115 0.00624452
0.02947717 0.03378293 0.02430551 0.00728374 0.01379422 0.01427509
0.04598223 0.0442411 0.07660514 0.03523894 0.05599355 0.08451695
0.01631541 0.0314934 0.00085946 0.0174912 0.04138379 0.02711384
0.06866315 0.01900861 0.03389626 0.00703161]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00320321 0.00245758 0.05081566 0.0019973 0.00120436 0.0178182
0.00900007 0.00408859 0.00048748 0.00486475 0.01992842 0.00354541
0.02097611 0.02124814 0.05100557 0.03342039 0.01004759 0.03045198
0.01334173 0.02570599 0.01152362 0.0055851 0.01331589 0.00670173
0.00111016 0.00674891 0.02190496 0.01318991 0.01012542 0.00536771
0.01056529 0.03104011 0.02194347 0.00570653 0.02260804 0.00449913
0.01584232 0.00856436 0.01271496 0.02217472 0.00201728 0.00743189
0.03465345 0.01961887 0.00071763 0.05253796 0.00713411 0.02597965
0.00044036 0.04210928 0.04276324 0.01934278 0.03413554 0.03117346
0.01696872 0.0103092 0.05179687 0.03101773 0.00419078 0.04059438
0.0681795 0.02798066 0.01269201 0.00019982 0.00546406 0.01333519
0.00925952 0.06136355 0.05846851 0.03697739 0.04446751 0.0707621
0.02017735 0.01044641 0.06713371 0.01791265 0.05612456 0.02375823
0.0050523 0.03568016 0.00771754 0.01959569 0.00679037 0.01420455
0.09201618 0.01115073 0.00372262 0.03688621 0.05250129 0.00520339
0.00423753 0.00267063 0.0575829 0.00228698 0.00253137 0.0280728
0.01264352 0.01788241 0.07061848 0.00521152]
</pre></div>
</div>
</div>
@@ -1709,15 +1710,15 @@ but now splitting the data into a training set and a test set.</p>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.99523653 0.19571155 3.70792235 2.56288447 -1.52977457]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.032142 0.27650047 3.22178183 2.62592637 -1.1157376 ]
Training R2
0.9957793953467874
0.994910550643694
Training MSE
0.009780101492052846
0.011016261190269798
Test R2
0.9948937588794076
0.9949909770903465
Test MSE
0.007661225358276448
0.007328424743352142
</pre></div>
</div>
</div>
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#
# 1. These lecture notes
#
# 2. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
# 2. [Video of lecture](https://youtu.be/VKakN-e4aUA)
#
# 3. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
# 3. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
#
# 4. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
# 4. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
#
# 5. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
# ## For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week
#
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@@ -375,6 +375,7 @@ MathJax.Hub.Config({
<ol>
<li> These lecture notes</li>
<li> <a href="https://youtu.be/VKakN-e4aUA" target="_self">Video of lecture</a></li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</li>
<li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
<li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>
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<ol>
<p><li> These lecture notes</li>
<p><li> <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a></li>
<p><li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</li>
<p><li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
<p><li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>
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<ol>
<li> These lecture notes</li>
<li> <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a></li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</li>
<li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
<li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>
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<ol>
<li> These lecture notes</li>
<li> <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a></li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</li>
<li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
<li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>
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@@ -17,6 +17,7 @@ o Monday: Ridge and Lasso regression and Singular Value Decomposition
=== Reading recommendations: ===
o These lecture notes
o "Video of lecture":"https://youtu.be/VKakN-e4aUA"
o Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
o Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
o For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.