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---
title: Machine Perception
ref: mp2023
ref: mp2024
description:
semester: Spring 2023
number: 263-3710-00L
Expand Down Expand Up @@ -62,384 +62,7 @@ <h3>Schedule</h3>
<th>Wk.</th><th>Date</th> <th>Content</th><th>Material</th> <th>Exercise Session</th>
</tr>
<tbody>
<!--
<tr>
<td>1</td>
<td> 22.02 </td>
<td>
<h5>Deep Learning Introduction</h5><p>Class content &amp; admin</p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l01-Intro.pdf">slides</a><br/>
</td>
<td>
</td>
</tr>
<tr>
<td>1</td>
<td> 23.02 </td>
<td>
<h5>-- No Class --</h5>
</td>
<td></td>
<td></td>
</tr>
<tr>
<td>2</td>
<td>01.03 <br> 02.03</td>
<td>
<h5>Training Neural Networks</h5><p>Backpropagation <br> Feedforward Networks, <br> Representation Learning</p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l02a-Intro-NN.pdf">slides pt. I</a><br/>
<!-- <a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l02a-Intro-NN-annotated.pdf">slides pt. I (annotated)</a><br/> -->
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l02b-Function_Approximation_Backpropagation.pdf">slides pt. II</a><br/>
<!-- <a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l02b-Function_Approximation_Backpropagation-annotated.pdf">slides pt. II (annotated)</a><br/> -->
<!-- <br/> -->
<a class="a-text-ext" href="https://colab.research.google.com/drive/1SrcuVZI-5OS74UrAGHRTeUCXy5XrvlS2?usp=sharing">Perceptron Visualization Notebook</a><br/>
<!--
<a class="a-vid" href="https://ethz.zoom.us/rec/share/p2fodabW9EuSTdAVQtoXb0xOGb0PWaZT0kbQWZr_-0o_622K9uMbeDZeDpbTpZ15.XepCi2ydOU5mLGSP">recording part I</a><br/>
<br/>
<a class="a-vid" href="https://ethz.zoom.us/rec/share/HkGJYgspQvEVSQ1SvDuzae5PVpnOQZCB8gMyO1aiysmNUE_xSEWt8s0yVpqElOti.saA0w8P8j4rI9G8H">recording part II</a><br/>-->
</td>
<td>
<p><b>Tutorial</b> Implement your own MLP</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_1.pdf">slides</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/117IIQPs6zigZM2xQg3MHJhN_pwahf4O6" target="_blank">XOR Notebook</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1a4XCUaUfiw41ueRfp86asO5aWq6TO6fF" target="_blank">XOR Solutions</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1Ihozrq_0CVOzk-NYv0KFOKuzW6X--78d" target="_blank">Eye-Gaze Notebook</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1oOMUnSigt2a0twnKSIYG3zirxBUYZUJN" target="_blank">Eye-Gaze Solutions</a><br/>
<p><b>Tutorial</b> Linear Regr.</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_2.pdf">slides</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1O3VVsHhEuNTOb6ALXQJa3OgwxLB2xM9u">Linear Regression Notebook</a><br/>
<!-- <br/> -->
<p><b>Pen &amp; Paper</b> Backprop.</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex2_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex2_solutions.pdf">exercise solution</a><br/>

</td>
</tr>
<tr>
<td>3</td>
<td>09.03. <br> 10.03.</td>
<td>
<h5>Convolutional Neural Networks</h5>
</td>
<td>
<p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l03a-CNN.pdf">slides pt. I</a><br/>
<!-- <a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/lectures/2022-MP-l03a-CNN-annotated.pdf">slides pt. I (annotated)</a><br/>-->
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l03b-CNN.pdf">slides pt. II</a><br/>
<!-- a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/lectures/2022-MP-l03b-CNN-annotated.pdf">slides pt. II (annotated)</a><br/> -->
</p>
<p>Additional material:
<br/>
<a class="a-text-ext" href="https://files.ait.ethz.ch/teaching/courses/2021-SS-Machine-Perception/downloads/reading_materials/RSVP.mp4">RSVP</a>
<br/>
<a class="a-text-ext" href="https://files.ait.ethz.ch/teaching/courses/2021-SS-Machine-Perception/downloads/reading_materials/Cortical-Neuron.mp4">Cortical Neuron</a> -->
</p>

</td>
<td>
<p><b>Tutorial</b> CNNs in Pytorch</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_3.pdf">slides</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1VB5CUiuAXAmPrumO1QJTMdq2gUBgdIm9">CNN Notebook</a><br/><br/>

<p><b>Pen &amp; Paper</b> CNN</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex3_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex3_solutions.pdf">exercise solution</a><br/>
</td>

</tr>

<tr>
<td>4</td>
<td>15.03.</td>
<td>
<h5> Fully Convolutional Neural Networks</h5>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l04-Fully-CNN.pdf">slides</a>
</td>
<td></td>
</tr>


<tr>
<td>4</td>
<td>16.03.</td>
<td>
<h5>Recurrent Neural Networks</h5><p>LSTM, GRU, Backpropagation through time</p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l05-RNN.pdf">slides</a><br/>
</td>

<td>
<p><b>Tutorial</b> RNNs in Pytorch</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_4.pdf">slides</a><br/>
<a class="a-text-ext" href="https://colab.research.google.com/drive/1jLJzlxIk6ELxAW-_OjUdpwzOiBB8B-mY">RNN Notebook</a><br/>
<br/>
<p><b>Pen &amp; Paper</b> RNN</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex4_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/exercises/ex4_solutions.pdf">exercise solution</a><br/>
</td>

</tr>

<tr>
<td>5</td>
<td>23.03. <br> 24.03 </td>
<td>
<h5>Generative Models Pt. I: Latent Variable Models </h5><p>Variational Autoencoders, etc.</p>
</td>
<td>
<p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l06a-VAE-Pt-I.pdf">slides pt. I</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l06b-VAE-Pt-II.pdf">slides pt. II</a>
</p>
</td>

<td>

<p><b>Class</b> Tips for Training I</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_5.pdf">slides</a><br/>
<br/>
<p><b>Pen &amp; Paper</b> VAE</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex5_vae_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/exercises/ex5_vae_solutions.pdf">exercise solution</a><br/>
</td>


</tr>

<tr>
<td>6</td>
<td>30.03. <br> 31.03.</td>
<td>
<h5>Generative Models Pt. II: Autoregressive Models </h5><p>PixelCNN, PixelRNN, WaveNet, Stochastic RNNs</p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l07a-AR-Pt-I.pdf">slides pt. I</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l07b-AR-Pt-II.pdf">slides pt. II</a>
<br/>
</td>
<td>
<p><b>Class</b> Tips for Training II</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_6.pdf">slides</a><br/>
<p><b>Pen &amp; Paper</b> AR</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex6_ar_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex6_ar_solutions.pdf">exercise solution</a><br/>
</td>

</tr>


<tr>
<td>7</td>
<td>05.04. <br> 06.04.</td>
<td>
<h5>Generative Models Pt. III: Normalizing Flows and Invertible Neural Networks </h5><p> </p>
</td>
<td>
<p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l08a-NF-Pt-I.pdf">slides NF Pt. I</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l08b-NF-Pt-II.pdf">slides NF Pt. II</a>
<br/>
</p>
<!-- <p>Additional material:
<br/>
<a class="a-text-ext" href="https://arxiv.org/pdf/1605.08803.pdf">RealNVP</a>
<br/>
<a class="a-text-ext" href="https://arxiv.org/abs/1410.8516">NICE</a>
<br/>
<a class="a-text-ext" href="https://arxiv.org/pdf/1807.03039.pdf">GLOW</a>
<br/>
<a class="a-text-ext" href="https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf">NeuralODE</a>
<br/>
<a class="a-text-ext" href="https://arxiv.org/pdf/1810.01367">FFJORD</a>
<br/>
<a class="a-text-ext" href="http://de.arxiv.org/pdf/2006.14200">SRFlow</a>
<br/>
<a class="a-text-ext" href="https://arxiv.org/abs/2008.02401 ">StyleFlow</a>
<br/>
<a class="a-text-ext" href="https://youtu.be/LRAUJUn3EqQw">StyleFlow (video)</a>
<br/>
<a class="a-text-ext" href="http://arxiv.org/abs/1912.07009">C-Flow</a>
<br/>
<a class="a-text-ext" href="https://www.youtube.com/watch?v=GkH-Msrhif0">C-Flow (video)</a>
</p> -->
</td>
<td>
<p><b>Pen &amp; Paper</b> NF</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex7_nf_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex7_nf_solutions.pdf">exercise solution</a><br/>
</td>
</tr>

<tr>
<td>8</td>
<td>12.04. <br> 23.04.</td>
<td>
<h5>-- No Class (Easter) --</h5>
</td>
<td></td>
<td></td>
</tr>

<tr>
<td>9</td>
<td>19.04. <br> 20.04.</td>
<td>
<h5>Generative Models Pt. IV: Implicit Models </h5><p>Generative Adversarial Networks &amp; Co</p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l09a-GAN-Pt-I.pdf">slides GAN Pt. I</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l09b-GAN-Pt-II.pdf">slides GAN Pt. II</a>
<br/>
</td>
<td>
<p><b>Tutorial</b> Exercise Discussion and Euler</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_7_PP.pdf">slides PP Backprop&CNN</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_7_Euler.pdf">slides Euler</a><br/>
<p><b>Pen &amp; Paper</b> GAN</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex8_gan_instructions.pdf">exercise</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex8_gan_solutions.pdf">exercise solution</a>
<br/>
</td>
</tr>
<tr>
<td>10</td>
<td>26.04 <br> 27.04</td>
<td>
<h5>Generative Models Pt. V: GAN Applications and Diffusion Models</h5>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l09c-GAN-Pt-III.pdf">slides GAN Pt. III</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l10a-DiffusionModels.pdf">slides Diffusion Models</a>
<br/>
</td>
<td>
<p><b>Pen &amp; Paper class</b> Diffusion Models</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex14_diff_instructions.pdf">exercise</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex14_diff_solutions.pdf">exercise solution</a>
<br/>
</td>
</tr>
<tr>
<td>11</td>
<td>03.05 <br> 04.05</td>
<td>
<h5>Implicit Surfaces and Neural Radiance Fields</h5>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l11a-NIR.pdf">slides NIR Pt. I</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l11b-NIR.pdf">slides NIR Pt. II</a>
<br/>
</td>
<td>
<p><b>Tutorial</b> Exercise Discussion AR</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_tutorial-9-ar.pdf">slides</a><br/>
<p><b>Pen &amp; Paper class</b> Implicit Surfaces</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex_12_instructions.pdf">exercise</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex_12_solutions.pdf">exercise solution</a>
<br/>
</td>
</tr>


<tr>
<td>12</td>
<td>10.05. <br> 11.05.</td>
<td>
<h5> Parametric Human Body Models and Applications </h5><p> </p>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l12-PBM.pdf">slides PBM </a>
<br/>
</td>
<td>
<p><b>Tutorial</b> Exercise Discussion GAN & NF </p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/MP2023_10_PP_GAN_NF.pdf">slides</a><br/>
<p><b>Pen &amp; Paper</b> PBM </p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/exercises/ex11_pbm_instructions.pdf">exercise</a>
<br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/exercises/ex11_pbm_solutions.pdf">exercise solutions</a>
<br/>
</td>
</tr>

<tr>
<td>13</td>
<td>17.05. <br> 18.05.</td>
<td>
<h5>-- No classes or exercise sessions --</h5>
</td>
<td>
</td>
<td>
</td>
</tr>

<tr>
<td>13</td>
<td>24.05. <br> 25.05.</td>
<td>
<h5>Reinforcement Learning</h5>
</td>
<td>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l12a-RL.pdf">slides RL pt. I</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2023-SS-Machine-Perception/downloads/lectures/2023-MP-l12b-RL.pdf">slides RL pt. II</a><br/>
</td>
<td>
<p><b>Pen &amp; Paper</b> RL</p>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/exercises/ex13_rl_instructions.pdf">exercise</a><br/>
<a class="a-pdf" href="https://files.ait.ethz.ch/teaching/courses/2022-SS-Machine-Perception/downloads/exercises/ex13_rl_solutions.pdf">exercise solutions</a><br/>
</td>
</tr>

<tr>
<td>14</td>
<td>01.06</td>
<td>
<h5>AIT Open House</h5>
</td>
<td> </td>
<td> </td>
</tr>

<tr>
<td></td>
<td>07.06</td>
<td>
<h5>Exam (13:30 - 16:30; HIL E 3, HIL E 4)</h5>
</td>
<td> </td>
<td> </td>
</tr>

<tr>
<td></td>
<td>27.07 / 28.07</td>
<td>
<h5>Exam Review (14:00 - 15:00; Location ML F 39)</h5>
</td>
<td> </td>
<td> </td>
</tr>
-->


</tbody></table>


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