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COGS-4230: Human & Machine Learning
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<meta property="og:description" content="This seminar explores the growing convergence between computational theories of human and machine learning. The course will focus on major theoretical frameworks including deep learning, Bayesian inference, information theory, and reinforcement learning, utilizing journal articles from both machine learning and cognitive science literatures. These topics will be critically evaluated from the perspective of how computational theories can be instantiated in cognitive systems. Both successes, and limitations, of current computational theories will be considered.">
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Human & Machine Learning
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COGS-4230
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<p>
This seminar explores the growing convergence between computational theories of human and machine learning. The course will focus on major theoretical frameworks including deep learning, Bayesian inference, information theory, and reinforcement learning, utilizing journal articles from both machine learning and cognitive science literatures. These topics will be critically evaluated from the perspective of how computational theories can be instantiated in cognitive systems. Both successes, and limitations, of current computational theories will be considered.
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4 credits
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Cross-listed with:
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<a class="course-pill" href="COGS-6230">COGS-6230 Human & Machine Learning</a>
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none
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<th></th>
<th class="spring season-label">Spring</th>
<th class="summer season-label" colspan="2">Summer</th>
<th class="fall season-label">Fall</th>
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<th colspan="2"></th>
<th class="summer2 midsum-label">(Session 1)</th>
<th class="summer3 midsum-label">(Session 2)</th>
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<tr>
<th class="year">2024</th>
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<td class="term fall offered">
<div class="view-container detail-view-container">
<span class="term-course-info">
<a href="https://sis.rpi.edu/rss/bwckctlg.p_disp_listcrse?term_in=202409&subj_in=COGS&crse_in=4230&schd_in="> Human & Machine Learning (4c)</a>
</span>
<ul class="prof-list">
<li>Christopher Robert Sims</li>
</ul>
<span class="course-capacity">
Seats Taken: 0/19
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<th class="year">2023</th>
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<th class="year">2022</th>
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<th class="year">2020</th>
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