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1 - 10 of 19 results for: SYMSYS ; Currently searching autumn courses. You can expand your search to include all quarters

SYMSYS 1: Minds and Machines (CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 200)

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win | Units: 4 | UG Reqs: WAY-FR, GER:DB-SocSci

SYMSYS 190: Senior Honors Tutorial

Under the supervision of their faculty honors adviser, students work on their senior honors project. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit

SYMSYS 191: Senior Honors Seminar

Recommended for seniors doing an honors project. Under the leadership of the Symbolic Systems program coordinator, students discuss, and present their honors project.
Terms: Aut | Units: 1 | Repeatable for credit

SYMSYS 195E: Experimental Methods (PSYCH 251)

Graduate laboratory class in experimental methods for psychology, with a focus on open science methods and best practices in behavioral research. Topics include experimental design, data collection, data management, data analysis, and the ethical conduct of research. The final project of the course is a replication experiment in which students collect new data following the procedures of a published paper. The course is designed for incoming graduate students in psychology, but is open to qualified students from other programs who have some working knowledge of the R statistical programming language. Requirement: Psych 10/ Stats 60 or equivalent
Terms: Aut | Units: 3

SYMSYS 195I: Image Systems Engineering (PSYCH 221)

This course is an introduction to digital imaging technologies. We focus on the principles of key elements of digital systems components; we show how to use simulation to predict how these components will work together in a complete image system simulation. The early lectures introduce the software environment and describe options for the course project. The following topics are covered and software tools are introduced:n- Basic principles of optics (Snell's Law, diffraction, adaptive optics).n- Image sensor and pixel designsn- Color science, metrics, and calibrationn- Human spatial resolutionn- Image processing principlesn- Display technologiesnA special theme of this course is that it explains how imaging technologies accommodate the requirements of the human visual system. The course also explains how image systems simulations can be useful in neuroscience and industrial vision applications. The course consists of lectures, software tutorials, and a course project. Tutorials and projects include extensive software simulations of the imaging pipeline. Some background in mathematics (linear algebra) and programming (Matlab) is valuable.nPre-requisite: EE 261 or equivalent. Or permission of instructor required.
Terms: Aut | Units: 1-3

SYMSYS 195T: Natural Language Processing & Text-Based Machine Learning in the Social Sciences (PSYCH 290, SOC 281)

Digital communications (including social media) are the largest data sets of our time, and most of it is text. Social scientists need to be able to digest small and big data sets alike, process it and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical ana more »
Digital communications (including social media) are the largest data sets of our time, and most of it is text. Social scientists need to be able to digest small and big data sets alike, process it and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical analysis of language variables in R. Familiarity with Python is helpful but not required. Basic familiarity with R is expected. The ability to wrangle data into a spreadsheet-like format is expected. A basic introduction to SQL will be given in the course. Familiarity with SSH and basic Linux is helpful but not required. Understanding of regression is expected.
Terms: Aut | Units: 4

SYMSYS 195V: Data Visualization (CS 448B)

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of CS147, CS148, or equivalent.
Terms: Aut | Units: 3-4 | Repeatable for credit

SYMSYS 196: Independent Study

Independent work under the supervision of a faculty member. Can be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

SYMSYS 197: Practicum in Teaching SymSys 1

The purpose of this practicum course is to prepare students to lead discussion sections of Minds and Machines ( SYMSYS 1 / CS 22 / LINGUIST 35 / PHIL 99 / PSYCH 35). The course will provide pedagogical training in the context of introductory cognitive science. Students will learn how to: implement strategies for effective discussion and engaging learning activities in section; effectively support students in 1:1 and small group learning; and consider a variety of strategies for student assessment.
Terms: Aut | Units: 3-4
Instructors: Krejci, B. (PI)

SYMSYS 200: Minds and Machines (CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1)

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win | Units: 4
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