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<!DOCTYPE html>
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<h1 class="center"> Cognitive Science at Cal</h1>
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<li class="active col-ss-6 center"><a href="academics.html">Academics Overview</a></li>
<li class="col-ss-6 center"><a href="office_hours.html">Office Hours</a></li>
<li class="col-ss-6 center"><a href="disciplines.html">The Disciplines</a></li>
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<p> This page houses a bunch of resources and information about cognitive science at UC Berkeley collected by the CSSA. The UC Berkeley cog sci program page <a href="http://cogsci.berkeley.edu/"><u> here</u></a> contains further details including major requirements and more. Here are some of the resources the CSSA has to offer: </p>
<ul>
<li><p> <a class="gotooffset1" href=".offset1"> <u> Berkeley Review of CogSci Articles (BROCA) Decal</u></a></p></li>
<li><p> <a class="gotooffset2" href=".offset2"><u> Academic Resources</u></a></p></li>
<li><p> <a class="gotooffset3" href=".offset3"><u> Frequently Asked Questions</u></a></p></li>
<li><p> <a href="./office_hours.html"><u> CSSA Office Hours</u></a></p></li>
</ul>
<p> If you ever have any questions about the major or classes feel free to come talk to us at meetings or office hours! The CSSA also puts on several informational events about the major such as <a href="./events.html"><u> Why Cog Sci?</u></a> or <a href="./events.html"><u> Telebears Night</u></a>. </p>
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<p class="center" style="margin-bottom: 0; padding-top: 5px"> Pictures from the CSSA's Why Cog Sci? event. </p>
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<h1 class="center"> Berkeley Review Of Cogsci Articles</h1>
<h3 class="center" style="color:#92ceb8"> Fall 2015: Wednesday 5 p.m. - 7 p.m. in 243 Dwinelle </h3>
<ul>
<li> <p> 2 unit decal organized by the CSSA. </p></li>
<li> <p> Students read weekly research papers and then discuss these papers with the authors (professors and graduate students at Berkeley). </p> </li>
<li> <p> Its goal is to get students comfortable reading and analyzing research papers as well as comfortable interacting with faculty and peers over research. </p></li>
<li> <p> It's a great way to get introduced to the vast array of research happening in cognitive science! </p> </li>
<li> <p> Multiple students have gotten research positions either through course assignments or from interacting with the guest speakers! </p> </li>
</ul>
<h3 class="center"> Learn more <u> <a href="http://www.decal.org/courses/3818">here</a></u> </h3>
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<h1 class="center"> Major Resources</h1>
<ul>
<h3> <li> <a href="http://cogsci.berkeley.edu/">UC Berkeley Cognitive Science Webpage</li></a></h3>
<ul>
<li> <p>For information on major requirements, honors thesis, and more.</p></li>
</ul>
<h3> <li> <a href="http://cogsci.berkeley.edu/people-0"> MacKenzie Moore: Academic Advisor </a></li> </h3>
<ul>
<li> <p> MacKenzie is the new academic advisor and central hub for everything cog sci. She handles major declaration, honors thesis details, academic progress checks, general advising, and more. </p> </li>
</ul>
<h3> <li><a href="http://cogsci.berkeley.edu/people-0">Terry Regier: Cognitive Science Program Director</li> </h3>
<ul>
<li> <p> Professor Regier handles course appeals for new cog sci electives.</p> </li>
</ul>
<h3> <li> <a href="./office_hours.html"> CSSA Office Hours</a></li></h3>
<ul>
<li> <p> Fall 2015: TBA </p></li>
</ul>
</ul>
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<h1 class="center"> General Academic Resources</h1>
<ul>
<h3> <li> <a href="http://www.berkeleytime.com/">Berkeley Time</a></li></h3>
<ul>
<li> <p> Very useful for viewing grades and enrollment history for classes you're interested in.</p></li>
</ul>
<h3> <li> <a href="http://ls-breadth.berkeley.edu/">UC Berkeley Breadth Requirements Search </a></li></h3>
<ul>
<li> <p> Useful search page for finding breadth courses. </p></li>
</ul>
<h3> <li> <a href="http://www.codecademy.com/">Codecademy </a></li></h3>
<ul>
<li> <p> A great place to gain familiarity with a programming language quickly (such as Python for 61A), particularly for those with no prior experience. </p></li>
</ul>
</ul>
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<h1 class="center"> Frequently Asked Questions </h1>
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<span class="number">01</span>
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<h3>What is the difference between the two computational modeling tracks?</h3>
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<p>Traditionally, there have been two main computational modeling tracks for the cognitive science major:</p>
<ol>
<h4> <li>CS 61A --> CS 70 --> CS 188 </li></h4>
<h4><li> E7 --> Math 55 --> Cog Sci 131 </li></h4>
</ol>
<p> However, <b>Cog Sci 131 is now taught in Python</b> and thus one of the following two tracks are recommended: </p>
<ol>
<h4> <li>CS 61A --> CS 70 --> CS 188 </li></h4>
<h4><li> CS 61A --> Math 55 --> Cog Sci 131 </li></h4>
</ol>
<p> Note that both tracks now recommend taking CS 61A as opposed to E7. Reasons are discussed in the next FAQ. </p>
<p> The first track is geared toward those who have a greater interest in computer science. This track is often pursued by those interested in doing a double major or minor and generally includes CS 61B as well (or more if pursuing a CS double or minor). The culminating class (188) is Artificial Intelligence taught in Python.</p>
<p> The second track tends to be the alternate track for those who are less interested in computer science. In general, this track is considered easier although it varies by person. The culminating class (131) is Computational Models of Cognition now taught in Python.</p>
<p> <b>Note that E7 can still be taken to satisfy the programming requirement for the major and it is fine if you have already taken it. CS 61A is now the recommended choice for intended majors, however. Also it is not necessary to specifically follow either of the paths above as long as you fulfill the requirements.</b> These are simply the two most common progressions. Some people more interested in the computational side of cognitive science take both CS 188 and Cog Sci 131. For more information on choosing between E7 and CS61A and between CS 70 and Math 55, read the next two FAQs.</p>
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<span class="number">02</span>
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<h3>Should I take Computer Science 61A or Engineering 7?</h3>
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<p> Now that Cog Sci 131 is being taught in Python, <b>it is strongly recommended that students take CS 61A instead of E7</b>. Since both 131 and 188 will be taught in Python now, CS 61A will adequately prepare you in Python as opposed to MATLAB in E7. E7 is also being much more strict about its math prerequisites (Math 1B), which many cognitive science students do not possess. For those wanting to learn the MATLAB language (perhaps for research purposes), CS 9A is a class specifically focused on the MATLAB programming language that also avoids the excess technical applications you would be taught in E7. </p>
<!-- <p>The following is a description of the two classes written by a cognitive science and computer science double major who has taken both E7 and CS 61A.</p>
<h4> What is the difference? </h4>
<ul>
<li> <p> CS 61A is NOT just a course on python and E7 is NOT just a course on MATLAB (for such courses see CS 9H and CS 9A respectively). Both are introduction to programming classes, but in different departments with different end goals. </p> </li>
<li> <p> Engineering 7 is "Introduction to Programming for Scientists and Engineers" and its primary goal is to equip students with the basic programming skills they believe an engineer/scientist should have. It uses the MATLAB programming language to do this because MATLAB is one of the more popular languages used for scientific/mathematical programming and it is a common language used in many labs here at Berkeley. The first half of the course teaches you the basics of programming (loops, functions, etc.) as well as the basics of the MATLAB language specifically. The second half of the course focuses on teaching you scientific programming applications such as linear algebra techniques, plotting data, regression/interpolation, and others. It assumes a certain level of mathematical maturity (requires Math 1B).</p></li>
<li> <p> Computer Science 61A is "The Structure and Interpretation of Computer Programs" and its primary goal is to introduce you to how a computer scientist creates and understands computer programs. It uses python to teach you programming basics (loops, functions, recursion, etc.), but it is not a python specific class (much less of one than E7 is a MATLAB specific class) and you actually learn a second language, Scheme, in the class as well. The course focuses much more on understanding how and why a program works rather than addressing specific applications as in E7 and also introduces you to major computer science concepts such as abstraction for example. </li> </p>
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<span class="number">03</span>
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<h3>Should I take Computer Science 70 or Math 55?</h3>
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<p> Math 55 and CS 70 both teach similar material from the same two fields: Discrete Mathematics and Probability Theory. Which class you should take is generally dependent upon which of the main progressions you are following. <b> If you are interested in CS 188 and the computational side more in general, then CS 70 is strongly recommended (indeed, CS 70 is a prerequisite for CS 188). On the other hand, Math 55 is recommended if you have little interest in the computational side of cognitive science. </b> In general, Math 55 is reported as being easier and less time-consuming, but this varies greatly by person and professor and both are difficult classes. </p>
<p>The following is a description of the two classes written by a cognitive science and computer science double major who has taken CS 70. The information on Math 55 is based on shared/observed information.</p>
<h4> What is the difference? </h4>
<ul>
<li><p> Both Math 55 and CS 70 are teaching the same core material, that is Discrete Mathematics and Basic Probability Theory. But these classes are in two different departments with two different end goals. </li> </p>
<li><p> Mathematics 55 is "Discrete Mathematics" and its primary goal is to equip students with a mathematical foundation in the field of discrete mathematics. Discrete Mathematics is the study of the mathematics of discrete phenomena - things that can be counted. At UC Berkeley, it entails formal logic and proofs, induction, set theory, graph theory, modular arithmetic, counting, basic probability, and more. As you can begin to tell, it covers a lot of different topics and gives you brief formal introductions to them. Being a math class, its emphasis is on understanding all of these topics at a basic formal mathematical level. </p></li>
<li><p> Computer Science 70 is "Discrete Mathematics and Probability Theory" and its primary goal is to equip students with a computer science understanding of topics from the same core set of topics as Math 55. However, it emphasizes in depth computer science applications of many of these topics and, as a result, covers fewer overall topics. There is a lot of overlap with Math 55, but more time is spent on computer science applications such as the stable marriage algorithm, hashing, load balancing, and so on. Many of the topics will be taught more in a computer science context as well (such as applying the principles to algorithms). <p></li>
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