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<!DOCTYPE html>
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<head>
<meta charset="UTF-8">
<title>Deep Learning for Computer Vision, Speech, and Language</title>
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<div class="nav-wrapper">
<a href="#">Columbia University EECS6894</a>
<ul id="nav-mobile" class="right">
<li><a href="index.html">Home</a></li>
<li><a href="schedule.html">Schedule</a></li>
<li><a href="homework.html">Homework</a></li>
<li><a href="project.html"> Project</a></li>
</ul>
</div>
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<div class="container">
<h2>Deep Learning for Computer Vision, Speech, and Language</h2>
<b> Time & Location </b><br>
7:00-9:30pm, Tuesday, Fall 2018 <br>
Mudd Building 633
<br><br>
<b> Co-taught by </b>
<ul>
<li> <a href="http://llcao.net">Liangliang Cao</a> (liangliang.cao_at_gmail.com)
</li>
<li> <a href="http://researcher.watson.ibm.com/researcher/view.php?person=us-cuix">Xiaodong Cui</a> (xdcuibruin_at_gmail.com)
</li>
<li> <a href="http://www.cs.columbia.edu/~kapil/">Kapil Thadani</a> (kapil_at_cs.columbia.edu) </li>
</ul>
<br><br>
<b> Guest Lecturers </b>
<ul>
<li> <a href="https://www.microsoft.com/en-us/research/people/leizhang/">Lei Zhang </a>
<li> <a href="https://researcher.watson.ibm.com/researcher/view.php?person=us-nccodell">Noel Codella</a>
<li> <a href="https://ece.illinois.edu/directory/profile/hshi10">Honghui Shi</a>
<li> <a href="http://andrewkae.com/">Andrew Kae</a>
<!--
<li> <a href="https://researcher.watson.ibm.com/researcher/view.php?person=us-weiz">Wei Zhang</a>
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</ul>
<br><br>
<b> Teaching Assistants </b>
<ul>
<li> Rajath Kumar (rm3497@columbia.edu): Handling Assignments 1 & 2</li>
<li> Qiao Zhang (qz2301@columbia.edu): Handling Assignments 3 & 4</li>
</ul>
<b> Office Hours </b>
<ul>
<li>Location: TA Room (Mudd 122A) </li>
<li>Friday - Qiao: 4:00 - 6:00 p.m. </li>
<li>Thursday - Rajath: 4:00 - 6:00 p.m. </li>
</ul>
<br><br>
<b> Course Introduction </b>
<p>
This graduate level research class focuses on deep learning techniques for vision, speech and
natural language processing problems.
It gives an overview of the various deep learning models and techniques, and surveys recent advances in the related fields.
Four homeworks and one final project with a heavy programming workload are expected.
</p>
<br><br>
<b> Programming </b>
<p>
This course uses <a href="https://www.tensorflow.org/">Tensorflow</a> as the primary programminging tool. However, other toolkits including pyTorch, or MxNet are also welcome.
</p>
<p>
All the programming problems in the homework should be done with <a href="https://ipython.org/notebook.html">IPython Notebook</a>.
Both code and experimenal results are required.
</p>
<p>
Google cloud will be used as the main programming platform. Note you can try <a href="colab.research.google.com">colab</a> as notebook with GPU installed. Students are also encouraged to install their computer with GPU cards.
</p>
<br><br>
<b> Grading </b>
<ul>
<li> <del>40%</del> 30% homework
<li> 20% paper presentation and course attendence
<li> <del>40%</del> 50% final project
</ul>
<br><br>
<b> Submission </b>
<ul>
<li> Homework should be uploaded on Coursework.
<li> Upload ipython-notebook instead of python file.
</ul>
<br>
<b> Recommended Books </b><br>
There is no required book for this class. But the following reading materials are recommended to read beyond the class:
<ul>
<li> Ian Goodfellow and Yoshua Bengio and Aaron Courville: <a href="http://www.deeplearningbook.org/">Deep Learning</a>
<li> Yoav Goldberg, <a href="https://www.amazon.com/dp/B071FGKZMH/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1">Neural Network Methods for Natural Language Processing</a>
</ul>
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