PSYC 496

Fall 2022 All Classes

All Classes

Adv Current Topics in Psych

SHOW FINAL EXAM SCHEDULE

Credit: 2 TO 4 hours.

Advanced treatment of current topics in the field of psychology.

2 to 4 undergraduate hours. 2 to 4 graduate hours. May be repeated to a maximum of 9 hours. Prerequisite: PSYC 100 and junior standing, or consent of instructor; particular sections may have additional 200-level and/or 300-level prerequisites.

My Favorites

My Schedule

Section Status updates every 10 minutes.

PSYC 496 class schedule data for fall 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
46958	Lecture-Discussion	AB1	11:00AM -12:15PM	WF	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Network Sci & its applications Section Info: Topic: Network Science & its applications Graduates should enroll in this section. Networks are ubiquitous and the emergence of complexity in biological and nonbiological systems is modelled using interactions among individual components of these systems. Network Science is an emerging discipline that deals with the scientific study of complex networks. In this course, students will be introduced to various types of networks in real world, will learn about the mathematical foundations of network measures, will be introduced to computational algorithms that are widely applied in network science, and will move on to learn statistical methods for analyzing network data. Students will acquire hands-on experience in manipulating, visualizing, and modeling the network data with various R packages. Students will also be introduced to applications of network science in various disciplines such as sociology, ecology, psychology/neuroscience, molecular biology and genetics, medicine, computer science, and information science. Prerequisites: Some familiarity with R language preferred. Restriction(s): Restricted to Graduate - Urbana-Champaign.
47772	Lecture-Discussion	AB2	11:00AM -12:15PM	WF	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Network Sci & its applications Section Info: Restrictions lifted on April 15 @ 5:00 pm. Topic: Network Science & its applications Undergraduate students should enroll in this section. Networks are ubiquitous and the emergence of complexity in biological and nonbiological systems is modelled using interactions among individual components of these systems. Network Science is an emerging discipline that deals with the scientific study of complex networks. In this course, students will be introduced to various types of networks in real world, will learn about the mathematical foundations of network measures, will be introduced to computational algorithms that are widely applied in network science, and will move on to learn statistical methods for analyzing network data. Students will acquire hands-on experience in manipulating, visualizing, and modeling the network data with various R packages. Students will also be introduced to applications of network science in various disciplines such as sociology, ecology, psychology/neuroscience, molecular biology and genetics, medicine, computer science, and information science. Prerequisites: Some familiarity with R language preferred. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
48376	Lecture-Discussion	ADG	1:00PM -2:50PM	WF	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Programming & Data Science w/R Section Info: Graduate Students should register for this section. In this course you will learn how to program in R and subsequently use R for effective data analysis and communication of results. The course will teach you the basics of R programming (such as data types and structures in R, writing functions in R, loops and iterations, etc.) and will expand to teach R packages to tidy, transform, visualize, and model your data. You will learn powerful visualization and transformation packages in R (ggplot2 and dplyr), and will also learn about interactive visualization packages in R. You will learn how to fit models to data with R packages and will move on to learn machine learning concepts/packages in R. This course will conclude by covering topics on tools for reproducible research. You will learn R packages such as R Markdown for integrating prose, code and results of data analysis. You will also learn version control with Git and GitHub, which will enable you to create and manage repositories of your code and share them for publication or collaborative purposes. This course will serve as a foundation course for any other advanced statistical analysis/modelling course in R that you may plan for later or in parallel. No prior programming experience is required for this course. Elective course for Intradisciplinary Psychology Concentration Restriction(s): Restricted to Graduate - Urbana-Champaign.
46959	Lecture-Discussion	ADH	1:00PM -2:50PM	WF	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Programming & Data Science w R Section Info: Undergraduate students should register for this section. In this course you will learn how to program in R and subsequently use R for effective data analysis and communication of results. The course will teach you the basics of R programming (such as data types and structures in R, writing functions in R, loops and iterations, etc.) and will expand to teach R packages to clean up, transform, analyze and visualize the data set and to further communicate your results. You will be introduced to powerful visualization and transformation packages such as ggplot2 and dplyr in R. This course will also cover the concepts and tools for reproducible research with packages in R such as Markdown and knitr for integrating prose, code and results. You will be introduced to version control with Git and GitHub, which will enable you to create and manage repositories of your code (and data) and share them for publication or collaborative purposes. This course will serve as a foundation course for any other advanced statistical analysis/modeling course in R that you may plan for later or in parallel. There are no prerequisites for this course. No programming experience is required for this course. Restrictions lifted on April 15 @ 5:00 pm. Elective course for Intradisciplinary Psychology Concentration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
37966	Lecture-Discussion	ID3	11:00AM -12:20PM	TR	29 Psychology Building	Longaker, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: IntroFaciltatngIntrGrpDialogue Section Info: Undergraduates should register for this section. Introduction to Facilitating Intergroup Dialogue Processes. This course is designed to give students a foundation in the skills and knowledge needed to facilitate culturally diverse group interactions. Course topics covered include: basic group facilitation skills, group dynamics; social identity group development; impacts of prejudice & stereotyping on groups; the dynamics and impact of power, privilege and social oppression on group interactions; facilitation of intergroup dialogue; and overviews of some contemporary intergroup issues. In addition, students who successfully complete this course will be eligible to apply for a position as a student-instructor for the EPSY 203: Social Issues Group Dialogue courses and/or as a Social Justice Educator. Elective course for Diversity Science or Intradisciplinary Psychology Concentration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
57400	Lecture-Discussion	ID4	11:00AM -12:20PM	TR	29 Psychology Building	Longaker, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: IntroFaciltatngIntrGrpDialogue Section Info: Introduction to Facilitating Intergroup Dialogue Processes. Restriction(s): Restricted to Graduate - Urbana-Champaign.
53248	Lecture-Discussion	JW	11:00AM -12:20PM	TR	313 Mumford Hall	Willits, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Knowledge Representation Section Info: Knowledge Representation: Surveys theories and data about the representation of knowledge by human beings; examines images, concepts, semantic features, propositions, semantic nets, rules, parallel distributed, procedural, schemas, mental models, and theories. Prerequisite: Background in either cognitive psychology, linguistics, or artificial intelligence. Elective course for BCOG major or PSYC major Intradisciplinary Psychology Concentration Restriction(s): Restricted to Undergrad - Urbana-Champaign.