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<h1 class="header__tagline">reverse engineering the human cognitive toolkit</h1>
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<h1>teaching</h1>
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<p>Here are some courses that Dr. Fan regularly teaches.</p>
<h3 id="psyc-230-computational-approaches-to-visual-abstraction">PSYC 230: Computational Approaches to Visual Abstraction</h3>
<p>From etchings on cave walls to digital media, the ability to convey abstract information in visual form is fundamental to many human innovations (e.g., painting, cartography, data visualization). This graduate seminar explores the problem of how humans produce and comprehend graphical representations, combining approaches from computational neuroscience, cognitive science, and artificial intelligence. Course participants will discuss both classic and contemporary work in these fields, work with modern computer-vision and human-behavioral datasets, and formulate an original research project to address outstanding questions. Previous experience using the Python programming language for scientific computing and prior coursework in neuroscience/psychology, calculus, and linear algebra is strongly preferred, but not strictly required, provided the student is willing to spend additional time learning foundational concepts on their own. <a href="https://docs.google.com/document/d/1Xb4pIUMWlMmvvGuEqDg-ci41P10x1izdZXjpG9xl8jM/edit?usp=sharing">Syllabus</a></p>
<h3 id="psyc-193l-science-of-learning-data-science">PSYC 193L: Science of Learning Data Science</h3>
<p>This is an advanced seminar and lab course on the science of learning as applied to statistical concepts & data science skills that are commonly used in modern psychological research. This course provides a scaffolded introduction to core statistical concepts and the use of R, a widely used statistical programming language. As part of the lab assignments and final project, you will work in groups to analyze real-world data and communicate your findings using the tools you have learned in the course. You will also have the opportunity to engage with the contemporary research literature on the teaching and learning of statistical reasoning. By taking this course, you are also contributing to improvements in the way that introductory statistics and data science skills are taught in Psychology at UCSD. <a href="https://science-of-learning-data-science.github.io/">Course Website</a></p>
<h3 id="psyc-193-perception--computation">PSYC 193: Perception & Computation</h3>
<p>This advanced undergraduate seminar explores how people perceive and understand the visual world, combining approaches from computational neuroscience, cognitive science, and artificial intelligence. Students will be introduced to both classic and contemporary work in these fields, and learn to work with modern computer-vision datasets. Prior coursework in statistics, calculus, linear algebra is strongly preferred, as well as prior experience using the Python programming language for scientific computing.
<a href="https://docs.google.com/document/d/1aBNfu9zDmHLs-xI1wZsOpjBoCjSQKvxD2ma9SUw4Y4g/edit?usp=sharing">Syllabus</a></p>
<h3 id="psyc-60-introduction-to-statistics">PSYC 60: Introduction to Statistics</h3>
<p>It is impossible to understand the modern world without an understanding of statistics. From public opinion polls to clinical trials in medicine to online systems that recommend purchases to us, statistics play a role in nearly every aspect of our lives. The goal of this course is to provide an understanding of essential concepts in statistics — how to construct models to explain variation in data — as well as the skills to apply these concepts to real data. <a href="https://psyc60.github.io/syllabus/">Syllabus</a></p>
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