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<title>COMS21202 - Symbols, Patterns and Signals - Computer Science Department UoB</title>
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<a href="https://www.ole.bris.ac.uk/webapps/blackboard/content/listContentEditable.jsp?content_id=_3923212_1&course_id=_237254_1" target="_blank">BLACKBOARD PAGE</a> |
<a href="http://www.bris.ac.uk/unit-programme-catalogue/UnitDetails.jsa?ayrCode=19%2F19&unitCode=COMS21202" target="_blank">UNIT INFO</a> |
<a target="_blank" href="https://www.ole.bris.ac.uk/webapps/discussionboard/do/forum?action=list_threads&course_id=_231522_1&nav=discussion_board_entry&conf_id=_204090_1&forum_id=_186076_1">FORUM</a>
<h1>COMS21202 - Symbols, Patterns and Signals</h1>
<link rel="stylesheet" href="simple.css" />
<a id="info"></a>
<hr/>
<h2>Unit Information</h2>
<p>This unit seeks to acquaint you with the fundamental aspects of processing digital data, presented in the context of concrete examples from applications in computer vision, graphics, speech, audio, machine learning and data mining. Particular emphasis is placed on the importance of representation and modelling.</p>
<hr/>
<h2> Staff</h2>
<table>
<tr><td class="Rui"><a href="http://neuralml.github.io/" target="_blank">Rui Ponte Costa (RPC)</a></td><td>office 3.26 MVB.</td></tr>
<tr><td class="Laurence"><a href="http://www.gatsby.ucl.ac.uk/~laurence/" target="_blank">Laurence Aitchison (LA)</a></td><td> office 3.16 MVB. <b>Unit Director</b> </td></tr>
<tr><td class="Majid"><a href="http://people.cs.bris.ac.uk/~majid/" target="_blank"> Majid Mirmehdi (MM) </a></td><td> office 3.11 MVB </td></tr>
</table>
<hr/>
<h2> Teaching Assistants</h2>
<p>Hazel Doughty, Will Price, Zeynel Samak, Daniel Davies, Xinyu Yang, Zhaozhen Xu, Daniel Gosden, Jonathan Munro, Vangelis Kazakos, Jian Ma</p>
<hr/>
<h2>Unit Materials</h2>
<table border="1" cellspacing="1" cellpadding="2">
<tr>
<td><i>Weeks</i></td> <td><i>Monday Lecture</i></td> <td><i>Wednesday Lecture</i></td> <td><i>Labs</i></td> <td><i>Thursday Lecture</i></td> <td><i>Assessments</i></td>
</tr>
<tr>
<td>13</td> <td class="Rui"><a href="RuiLectures/Lec1-handout.pdf" target="_blank">Data, Data Modelling and Estimation (I)</a></td>
<!-- <td class="Rui">Data, Data Modelling and Estimation (II)</td> -->
<td class="Rui"><a href="RuiLectures/Lec2-handout.pdf" target="_blank">Data, Data Modelling and Estimation (II)</a></td>
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_1" target="_blank">Intro to Jupiter Notebook I</a></td>
<!-- <td class="Rui"> <a href="RuiPCs/problemSheet1.pdf">Problem Class - Data Acquisition<br/></a> </td> -->
<td class="Rui"> <a href="RuiPCs/problemSheet1.pdf" target="_blank">Problem Class - Data Acquisition</a><br/><a href="RuiPCs/problemSheet1-ansC.pdf" target="_blank">answers</a> </td>
<td class="blank"> - </td>
</tr>
<tr>
<td>14</td>
<!-- <td class="Rui">Data Modelling and Estimation (III)</td> -->
<td class="Rui"><a href="RuiLectures/Lec3-handout.pdf" target="_blank">Data Modelling and Estimation (III)</a></td>
<td class="Rui"> <a href="RuiPCs/problemSheet2.pdf" target="_blank">Problem Class - Deterministic Data Modelling</a><br/><a href="RuiPCs/problemSheet2-ansC.pdf" target="_blank">answers</a></td>
<!-- <td class="Rui"> <a href="RuiPCs/problemSheet2.pdf">Problem Class - Deterministic Data Modelling</a><br/><a href="RuiPCs/problemSheet2-answers.pdf">answers</a></td> -->
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_2" target="_blank">Intro to Jupiter Notebook II</a></td>
<td class="Rui"><a href="RuiLectures/Lec4-handout.pdf" target="_blank">Data Modelling and Estimation (IV)</a></td>
<!-- <td class="Rui"><a href="RuiLectures/Lec4-handout.pdf">Data, Data Modelling and Estimation (IV)</a></td> -->
<td class="blank">-</td>
</tr>
<tr>
<td>15</td>
<!-- <td class="Rui">Data Modelling and Estimation (V)</td> -->
<td class="Rui"><a href="RuiLectures/Lec5-handout.pdf" target="_blank">Data Modelling and Estimation (V)</a></td>
<td class="Rui"><a href="RuiPCs/problemSheet3.pdf" target="_blank">Problem Class - Probabilistic Data Modelling</a><br/><a href="RuiPCs/problemSheet3-ansC.pdf" target="_blank">answers</a></td>
<!-- <td class="labs">Least Squares</td> -->
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_3" target="_blank">Least Squares</a></td>
<td class="Rui">Review part I<br/></td>
<!-- <td class="Rui"><a href="RuiPCs/part1-test19.pdf">Review part I</a><br/><a href="RuiPCs/part1-test19-answers.pdf">answers</a></td> -->
<td class="blank"> CW1 (set) </td>
</tr>
<tr>
<!--
<td>16</td>
<td class="Laurence"><a href="SPS-L01-Classification.pdf">Classification I</a></td>
<td class="Laurence"><a href="SPS-L02-Classification.pdf">Classification II</a></td>
<td class="labs">Maximum Likelihood</td>
<td class="Laurence"><a href="SPS-L03-Clustering.pdf">Clustering</a></td>
<td class="blank"> -</td>
-->
<td>16</td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Regression </a></td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Overfitting and regularisation </a></td>
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_4" target="_blank">Maximum Likelihood </a></td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Classification </a> </td>
<td class="blank"> -</td>
</tr>
<tr>
<!--
<td>17</td>
<td class="Laurence">Problem Class <a href="SPS-P01-Classification.pdf">(slides)</a>, <a href="problemsheet4.pdf">(sheet)</a>, <a href="problemsheet4a.pdf">(solutions)</a> </td>
<td class="Laurence"><a href="SPS-L04-GaussianMixture.pdf">Gaussian Mixture Methods</a></td>
<td class="labs">Fitting</td>
<td class="Laurence"><a href="SPS-L05-Evaluation.pdf">Evaluation Methods</a></td>
<td class="blank"> CW (deadline) </td>
-->
<td>17</td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Unsupervised learning </a> </td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Problem Class 1 </a></td>
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_5" target="_blank">Fitting </a> </td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Clustering </a> </td>
<td class="blank"> - </td>
</tr>
<tr>
<td>18</td>
<td colspan="4" class="blank"><b>Computer Science Explore Week</b></td>
<td class="blank"> - </td>
</tr>
<tr>
<!--
<td>19</td>
<td class="Laurence">Problem class (<a href="problemsheet6.pdf">sheet</a> <a href="problemsheet6a.pdf">Answers</a>)</td>
<td class="Laurence"><a href="PF/problemsheet6.pdf">Problem Class - More Classification and Clustering</a><br/>
<a href="PF/problemsheet6a.pdf">answers</a>
</td>
<td class="labs">Classification</td>
<td class="Laurence">Review part II</td>
<td class="blank"> CW2 (set) </td>
</tr>
-->
<td>19</td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Bayesian methods </a> </td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Problem Class 2 </a> </td>
<td class="labs"><a href="https://github.com/UoB-COMS21202/lab_sheets_public/tree/master/lab_6" target="_blank">Classification </a> </td>
<td class="Laurence"> <a href="https://github.com/LaurenceA/intro_lectures" target="_blank"> Review part II </a> </td>
<td class="blank"> - </td>
</tr>
<tr>
<td>20</td>
<td class="Majid"><a href="MajidLectures/01Intro.pdf">Representations, Transformations & Features (RTF): Intro/Overview</a> <p>See 1st video at bottom of this page.</p></td>
<td class="Majid"><a href="MajidLectures/02FourierSeries.pdf">RTF: Fourier Analysis (I)</a><p>See 2nd and 3rd videos.</p></td>
<td class="labs">-</td>
<td class="Majid">Term ended early.</td>
<td class="blank"> - </td>
</tr>
<tr>
<td colspan="6" class="blank"><b>Easter Break</b></td>
</tr>
<tr>
<td>21</td>
<td class="Majid"><a href="MajidLectures/03FFT.pdf">RTF: Fourier Analysis (II)</a><p>See 4th and part of 5th videos.</p> <p><a href="MajidLectures/fft2.py">FFT2 in Python</a></p> </td>
<td class="Majid"><a href="MajidPCs/problemsheetMM1.pdf">Problem Class I</a><p><a href="MajidPCs/problemsheetMM1A.pdf">Answers</a> Also see 2nd part of 5th video.</p></td>
<td class="labs">-</td>
<td class="Majid"><a href="MajidLectures/04Features.pdf">RTF: Feature Extraction</a><p>See 6th and 7th videos, ignore the part on a 2nd CW.</p></td>
<td class="blank"> CW1 (deadline) <p><b> Extended to May 22nd</b> </p> </td>
</tr>
<tr>
<td>22</td>
<td class="Majid"><a href="MajidLectures/05PCA.pdf">RTF: Dimensionality Reduction</a> <p> See 8th video</p> <p>PCA example: <a href="MajidLectures/dopca.m">Matlab</a> <a href="MajidLectures/pca.py">Python</a></p></td>
<td class="Majid"><a href="MajidPCs/problemsheetMM2.pdf">Problem Class II</a><p><a href="MajidPCs/problemsheetMM2A.pdf">Answers</a></p> </td>
<td class="labs"> - </td>
<td class="Majid"> - </td>
<td class="blank"> - </td>
<!-- <td class="blank"> CW2 (formative) </td> -->
</tr>
<tr>
<td>23</td>
<td class="Rui">Review part I (Rui)</td>
<!-- <td class="Rui"><a href="RuiLectures/Lec-Review.pdf">Review part I (Rui)</a></td> -->
<td class="Laurence">Review part II (Laurence)</td>
<td class="labs"> - </td>
<td class="Majid">Review Part III (Majid)</td>
<!-- <td class="Majid"><a href="MajidLectures/r1">Review Part III (Majid)</a></td> -->
<td class="blank"> - </td>
</tr>
<tr>
<td>24</td> <td colspan="5" class="blank"><b>Review week</b></td>
</tr>
</table>
<hr/>
<h2>Assessment Details</h2>
<p>The unit is assessed 40% coursework and 60% exam:</p>
<ol>
<li> <a href="https://github.com/UoB-COMS21202/COMS21202.github.io/tree/master/CW1" target="_blank">Link to CW1</a></li>
<li> CW1 [Wk15-21] - <b>Code + report</b> 40%</li>
<!-- <li> CW2 [Wk23] - Voluntary 0% [code + report] </li> -->
<li> Exam - 60% [Multiple Choice]</li>
</ol>
<hr/>
<h2>Lab Work</h2>
<h3>Installation Instructions:</h3>
<p><b>Jupiter Notebook - </b> For all COMS21202 needs you are encouraged to install <a href="https://www.continuum.io/">Anaconda <b>(Python 3.7)</b></a> as it bundles all the course's requirements. <i>Alternatively for manual installation, you will require Python 3.7.x with 'Jupyter' and 'iPython' both possibly in version 4.x.x as well as the following packages: 'Pillow', 'scikit-image', 'matplotlib', 'numpy', 'scipy', 'scikit', 'pygments' and 'scikit-learn'</i></p>
<p>In the lab Linux machines, you should be able to just run Jupyter Notebook with this command <br/>$ /opt/anaconda3-4.4.0/bin/jupyter notebook</p>
<br/>
<b><i>Programming in a Browser</i></b><br/>If you feel as akward as me about the idea about coding in a webbrowser you can use Emacs to render Jupiter notebooks. This way you get the finest of editing while at the same time having the benefit of Jupiter. Have a look at this repository on how to make it work <a href="https://tkf.github.io/emacs-ipython-notebook/">URL</a>. Here is a video showing how it can be done <a href="https://www.youtube.com/watch?v=dgcBKz03lK8">URL</a>.
<h2>Github</h2>
<p>All technical resources will be posted on the
<a href="https://github.com/uob-coms21202" target="_blank">COMS21202 Github organisation</a>. If you find any issues, please kindly raise an issue in the respective repository.
</p>
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