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            <h2>COMPSCI 682 Neural Networks: A Modern Introduction</h2>
            <p id="classdesc">This 3-credit course will focus on modern, practical methods for deep learning.
            The course will begin with a description of simple classifiers such as perceptrons
            and logistic regression classifiers, and move on to standard neural networks,
            convolutional neural networks, and some elements of recurrent neural networks,
            such as long short-term memory networks (LSTMs). The emphasis will be on understanding
            the basics and on practical application more than on theory. Most applications
            will be in computer vision, but we will make an effort to cover some natural language
            processing (NLP) applications as well, contingent upon TA support. The current
            plan is to use Python and associated packages such as <a href="http://www.numpy.org/">Numpy</a>
            and <a href="https://www.tensorflow.org/">TensorFlow</a>.
            Prerequisites include Linear Algebra, Probability and Statistics, and Multivariate
            Calculus. Some assignments will be in Python and some in C++.</p>


<h4>Accommodation Statement</h4>
<p id="statement">The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students.  If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course.  If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.</p>

<h4>Academic Honesty Statement</h4>
<p id="statement">Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst.  Academic dishonesty is prohibited in all programs of the University.  Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty.  Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty.  Instructors should take reasonable steps to address academic misconduct.  Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible.  Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair.  Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (<a href="http://www.umass.edu/dean_students/codeofconduct/acadhonesty/">http://www.umass.edu/dean_students/codeofconduct/acadhonesty/</a>).</p>


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                <h3>Instructor</h3>
                <div class="instructor2">
                  <a href="https://www.cs.umass.edu/~elm">
                    <div class="instructorphoto"><img src="/assets/fig/elm.jpg"></div>
                    <div>Prof. <NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Thu 3-4pm, CS248</div>-->
                </div>
              </div>
                
              <div class="col-xs-6 col-sm-6">
                <h3>Teaching Assistant</h3>
                <div class="instructor2">
                  <a href="https://www.cs.umass.edu/~hsu">
                    <div class="instructorphoto"><img src="/assets/fig/hsu.png"></div>
                    <div><NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Mon &amp; Tue 4-5pm, CS256A</div>-->
                </div>
                <div class="instructor2">
                  <a href="https://people.cs.umass.edu/~pbideau/">
                    <div class="instructorphoto"><img src="/assets/fig/bideau.png"></div>
                    <div><NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Mon &amp; Tue 4-5pm, CS256A</div>-->
                </div>
              </div>
                
            </div>
          </div>
          <div class="row">
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              <h2>Lectures</h2>
              <p>Fall 2017<br>
              Tue &amp; Thur 08:30-09:45 am<br>
              Hasbrouck 134 </p>
              <!--<p><a class="btn btn-default" href="#" role="button">View details &raquo;</a></p>-->
            </div><!--/.col-xs-6.col-lg-4-->
            <div class="col-xs-6 col-lg-4">
              <h2>Grading Policy</h2>
              <p>Assignment #1: 15%<br>
              Assignment #2: 15%<br>
              Assignment #3: 15%<br>
              Midterm: 15%<br>
              Final Project: 40%<br> </p>
              <!--<p><a class="btn btn-default" href="#" role="button">View details &raquo;</a></p>-->
            </div><!--/.col-xs-6.col-lg-4-->
            <div class="col-xs-6 col-lg-4">
              <h2>Course Discussions</h2>
              <p class="infoblk">Participate discussions on <a href="https://piazza.com/class/j6y7qcm8yf67iw">Piazza</a>,
              and use <a href="https://hypothes.is/">hypothes.is</a> extension to annotate online course notes inline. </p>
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            </div><!--/.col-xs-6.col-lg-4-->

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              <h2>Assignments</h2>
              <p class="infoblk">There will be 3 homework assignments through the semester. </p>
              <p><a class="btn btn-default" href="/assignments.html" role="button">View details &raquo;</a></p>
            </div><commit_msg>fixed piazza link<commit_after><!DOCTYPE html>
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            <button type="button" class="btn btn-primary btn-xs btn-umass" data-toggle="offcanvas">Toggle Announcements</button>
          </p>
          <div class="jumbotron">
            <h2>COMPSCI 682 Neural Networks: A Modern Introduction</h2>
            <p id="classdesc">This 3-credit course will focus on modern, practical methods for deep learning.
            The course will begin with a description of simple classifiers such as perceptrons
            and logistic regression classifiers, and move on to standard neural networks,
            convolutional neural networks, and some elements of recurrent neural networks,
            such as long short-term memory networks (LSTMs). The emphasis will be on understanding
            the basics and on practical application more than on theory. Most applications
            will be in computer vision, but we will make an effort to cover some natural language
            processing (NLP) applications as well, contingent upon TA support. The current
            plan is to use Python and associated packages such as <a href="http://www.numpy.org/">Numpy</a>
            and <a href="https://www.tensorflow.org/">TensorFlow</a>.
            Prerequisites include Linear Algebra, Probability and Statistics, and Multivariate
            Calculus. Some assignments will be in Python and some in C++.</p>


<h4>Accommodation Statement</h4>
<p id="statement">The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students.  If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course.  If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.</p>

<h4>Academic Honesty Statement</h4>
<p id="statement">Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst.  Academic dishonesty is prohibited in all programs of the University.  Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty.  Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty.  Instructors should take reasonable steps to address academic misconduct.  Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible.  Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair.  Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (<a href="http://www.umass.edu/dean_students/codeofconduct/acadhonesty/">http://www.umass.edu/dean_students/codeofconduct/acadhonesty/</a>).</p>


            <div class="row">
              <div class="col-xs-6 col-sm-6">
                <h3>Instructor</h3>
                <div class="instructor2">
                  <a href="https://www.cs.umass.edu/~elm">
                    <div class="instructorphoto"><img src="/assets/fig/elm.jpg"></div>
                    <div>Prof. <NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Thu 3-4pm, CS248</div>-->
                </div>
              </div>
                
              <div class="col-xs-6 col-sm-6">
                <h3>Teaching Assistant</h3>
                <div class="instructor2">
                  <a href="https://www.cs.umass.edu/~hsu">
                    <div class="instructorphoto"><img src="/assets/fig/hsu.png"></div>
                    <div><NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Mon &amp; Tue 4-5pm, CS256A</div>-->
                </div>
                <div class="instructor2">
                  <a href="https://people.cs.umass.edu/~pbideau/">
                    <div class="instructorphoto"><img src="/assets/fig/bideau.png"></div>
                    <div><NAME></div>
                  </a>
                  <div>Office Hours: TBD</div><!--<div>Office Hours: Mon &amp; Tue 4-5pm, CS256A</div>-->
                </div>
              </div>
                
            </div>
          </div>
          <div class="row">
            <div class="col-xs-6 col-lg-4">
              <h2>Lectures</h2>
              <p>Fall 2017<br>
              Tue &amp; Thur 08:30-09:45 am<br>
              Hasbrouck 134 </p>
              <!--<p><a class="btn btn-default" href="#" role="button">View details &raquo;</a></p>-->
            </div><!--/.col-xs-6.col-lg-4-->
            <div class="col-xs-6 col-lg-4">
              <h2>Grading Policy</h2>
              <p>Assignment #1: 15%<br>
              Assignment #2: 15%<br>
              Assignment #3: 15%<br>
              Midterm: 15%<br>
              Final Project: 40%<br> </p>
              <!--<p><a class="btn btn-default" href="#" role="button">View details &raquo;</a></p>-->
            </div><!--/.col-xs-6.col-lg-4-->
            <div class="col-xs-6 col-lg-4">
              <h2>Course Discussions</h2>
              <p class="infoblk">Participate discussions on <a href="https://piazza.com/umass/fall2017/compsci682/home">Piazza</a>,
              and use <a href="https://hypothes.is/">hypothes.is</a> extension to annotate online course notes inline. </p>
              <p><a class="btn btn-default" href="https://piazza.com/umass/fall2017/compsci682/home" role="button">Discussion Forum &raquo;</a></p>
            </div><!--/.col-xs-6.col-lg-4-->

            <div class="col-xs-6 col-lg-4">
              <h2>Assignments</h2>
              <p class="infoblk">There will be 3 homework assignments through the semester. </p>
              <p><a class="btn btn-default" href="/assignments.html" role="button">View details &raquo;</a></p>
            </div>
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