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<li><a href="projects.html#3">Building an Automated Machine Learning Algorithm for Characterization of Anxiety-related Vocalizations in Non-human Primates</a></li>
<li><a href="projects.html#4">Identifying Active Extravasation on Arteriograms using Machine
Learning</a></li>
<li><a href="projects.html#5">Application of Machine Learning to CT Characterization of Renal Cell
Carcinoma</a></li>
<li><a href="projects.html#6">Categorizing Dendritic Spine Morphology</a></li>
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<h1>Study Groups</h1>
<h2>A List Of All Our Active Study Groups</h2>
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<ul class="contents">
<!-- <li><a href="studyGroups.html#niagi">Neuro-Inspired Artificial General Intelligence</a></li> -->
<li><a href="studyGroups.html#casg">Causal Architecture Study Group</a></li>
<li><a href="studyGroups.html#dlsg">Deep Learning Study Group</a></li>
<li><a href="studyGroups.html#iaisg">Introduction to AI Study Group</a></li>
<li><a href="studyGroups.html#eaisg">Ethical AI Study Group</a></li>
</ul>
<br>
<hr>
<h2>Active Study Groups</h2>
<!-- <h3><span id="ml"></span>Machine Learning</h3>
<p>Study group focusing around Andrew Ng’s machine learning course on Coursera. The course material is completed on an independent basis, but the group meetings provide a space to ask questions and discuss the course insofar as the group has progressed in it. Meetings take place every weekend; the Slack channel will contain details about the time and location.</p>
<p><strong>Group Leader:</strong> Soham Dasgupta <br />
<strong>Slack channel:</strong> study-ml<br />
<strong>Contact:</strong> @Soham Dasgupta (Slack), <a href="mailto: [email protected]">[email protected]</a></p> -->
<!-- <h3><span id="niagi"></span>Neuro-Inspired AI</h3>
<p>This study group will focus on discussing the interactions between the fields of cognitive science,
computational neuroscience, and artificial intelligence. We will primarily focus our learning materials on
research papers, and are always looking for additional members willing to present papers throughout the
semester (we are willing to suggest some options).<br>
</p>
<p>A few concrete subtopics we’d like to touch on throughout the semester(s)</p>
<ul>
<li>Origin and evolution of deep learning and reinforcement learning algorithms</li>
<li>Theories and evidence regarding how the brain updates its feature maps over time</li>
<li>How plastic is the neocortex at each level of the cortical hierarchy?</li>
<li>Does backprop occur in the brain? What biologically plausible alternatives to backprop exist?</li>
<li>How can theoretical neuroscience help reveal mechanisms/algorithms of AGI?</li>
<li>What key components are lacking from AI these days that prevent us from achieving AGI (e.g. how is the
brain different from a DQN algorithm)?</li>
</ul>
<p><strong>Time-Commitment:</strong> 2 hours/week<br />
<strong>Contact Info:</strong>#sg-neuro-inspired; @Chris Endemann (Slack), <a
href="mailto:[email protected]">[email protected]</a><br />
<strong>Prereqs:</strong>Previous experience with deep learning is helpful, but not required.
<br />
<strong>Meeting time/location:</strong>Mondays @ 5:30pm, Memorial Union</p> -->
<!-- <h3><span id="rl"></span>Reinforcement Learning</h3>
<p>Study reinforcement learning as a team! This semester (Spring 2019) we'll be reading an intro book on RL, and meeting
weekly to discuss a few chapters at a time. We meet Sundays at Rhetas from 3:45-5pm. Join our Slack channel to see which
chapters will be discussed next.</p>
<p><strong>Group Leader:</strong> Chris Endemann <br />
<strong>Slack channel:</strong> study-rl <br />
<strong>Contact:</strong> @Chris Endemann (Slack), <a href="mailto: [email protected]">[email protected]</a>
</p> -->
<h3><span id="dlsg"></span>Deep Learning Study Group:</h3>
<p>This study group will be focusing on working through Andrew Ng’s Deep Learning specialization on Coursera.
This study group is a good way to break into the world of AI. In the five courses in the specialization, you
will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead
successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam,
Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare,
autonomous driving, sign language reading, music generation, and natural language processing. You will
master not only the theory, but also see how it is applied in industry. You will practice all these ideas in
Python and in TensorFlow, which the courses will teach.</p>
<p><strong>Time-Commitment:</strong> 2-3 hours/week<br />
<strong>Contact Info:</strong>#sg-deep-learning; @Vishnu Yarlagadda (Slack); @Katherine Fu (Slack); <a
href="mailto:[email protected]">[email protected]</a><br />
<strong>Prereqs:</strong>Previous experience with basic linear algebra (matrices) is helpful, but not
required<br />
<strong>Meeting time/location:</strong>Mondays @ 7:30, Location: Check Slack</p>
<h3><span id="casg"></span>Causal Architecture Study Group:</h3>
<p>To effectively engage with the world, AI must reckon with information and causality. Further, the design of
innovative artificially intelligent systems may improve by drawing inspiration from the remarkable causal
architecture of some of the systems it seeks to imitate: living organisms and conscious minds. As such, we
will approach this topic from several vantage points, including converging scientific theories of
consciousness and the origin of life. We will assume no prior knowledge and build up the requisite
mathematics and information theory as we go. The goal is to explore artificial intelligence as a “causally
emergent” phenomena (see Agents Above, Atoms Below) through a discussion of research papers and topic
primers.</p>
<p><strong>Time-Commitment:</strong>0-4 hours/week<br />
<strong>Contact Info:</strong> #sg-causal-arch; @Alex Plum (Slack), <a
href="mailto:[email protected]">[email protected]</a><br />
<strong>Prereqs:</strong>None! Previous experience with probability theory, information theory, and
stochastic processes is helpful, but not required.<br />
<strong>Meeting time/location:</strong>Wednesdays @ 7:30, Location: Check Slack</p>
<h3><span id="iaisg"></span>Introduction to AI Study Group:</h3>
<p>Working through material from the book Artificial Intelligence: A Modern Approach by Russell and Norvig.
There will be a decent overlap with the material taught in UW-Madison’s CS 540: Introduction to artificial
intelligence, but we won't focus on the programming side of things unless people really want to. Great for
people who want a solid conceptual foundation of classical AI techniques.</p>
<p><strong>Time-Commitment:</strong>1-3 hours/week<br />
<strong>Contact Info:</strong>#sg-intro-ai; @Nickolas Comeau (Slack), <a
href="mailto:[email protected]">[email protected]</a> or Jules Vigy / Shri Shruthi Shridhar on slack<br />
<strong>Prereqs:</strong>Some background in calculus helpful, but no hard prerequisite knowledge
requirements.<br />
<strong>Meeting time/location:</strong>Sundays @ 6:00, Location: Computer Sciences room 1257</p>
<h3><span id="eaisg"></span>Ethical AI Study Group: </h3>
<p>This club-wide study group has been enacted to help the club establish several documents related to ethical
guidelines in the tech industry. EVERYONE is encouraged to participate. We are making the slack channel for
this group one of the default channels on slack so that as many club members as possible have a chance to
respond to our polls. Soon, we will post a document to help summarize a reasonable and agreed upon set of
standards to help mitigate risk associated with different platforms/apps/practices. Some of our core topics
of interest include:</p>
<ul>
<li>User-Validation: Are bots a risk or not? How difficult is it to detect bots on social media platforms?
</li>
<li>Algorithm Use: When should open source or complete transparency be encouraged/required
<li>
<li>Data Use: How transparent should data-use be? What is a reasonable way to communicate such use? </li>
<li>Security standards: how can we protect user-data?</li>
<li>Model Interpretability: When to avoid black-box models</li>
<li>Bias: How to detect and when to intervene - content generators, classifiers</li>
<p><strong>Time-Commitment:</strong>10 minutes/week<br />
<strong>Contact Info:</strong>#sg-ethical-ai; @Chris Endemann (Slack), <a
href="mailto:[email protected]">[email protected]
</a><br />
<strong>Prereqs:</strong>None<br />
<strong>Meeting time/location:</strong>Currently there are no meeting times.</p>
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