printer friendly pagePSYCH 147S: Introduction to the Psychology of Emotion
Our emotions influence how we perceive the world, inform how we make critical life decisions, and connect us with other people. Affective science, the scientific study of emotion, investigates how emotions shape our lives. In this course, we explore how emotions arise as feelings we experience, behaviors we commit, and physiological reactions to our environments. Across these levels of analysis, we will consider how emotions interact with our personalities, past experiences, future goals, stages of development, and socio-cultural surroundings. We will learn how affective science has clarified the nature of emotion, how emotions evolved across diverse animal species, and how emotions impact our health and relationships with others. You will leave this class with an improved, scientifically-informed understanding of your own and others emotions, and strategies for how to effectively use and manage your feelings in daily life.
Terms: Sum
| Units: 3
Instructors:
Goldenberg, A. (PI)
PSYCH 155: Introduction to Comparative Studies in Race and Ethnicity (CSRE 196C, ENGLISH 172D, SOC 146, TAPS 165)
How different disciplines approach topics and issues central to the study of ethnic and race relations in the U.S. and elsewhere. Lectures by senior faculty affiliated with CSRE. Discussions led by CSRE teaching fellows. Includes an optional Haas Center for Public Service certified Community Engaged Learning section.
Terms: Win
| Units: 5
| UG Reqs: GER:DB-SocSci, GER:EC-AmerCul, WAY-EDP, WAY-SI
Instructors:
Markus, H. (PI)
;
Moya, P. (PI)
;
Bencharit, Y. (TA)
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Instructors:
Markus, H. (PI)
;
Moya, P. (PI)
;
Bencharit, Y. (TA)
;
Hill, R. (TA)
;
Muragishi, G. (TA)
;
Patterson, C. (TA)
;
Sanchez, K. (TA)
PSYCH 221: Image Systems Engineering
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.nThe 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
Instructors:
Wandell, B. (PI)
;
Lian, T. (TA)
PSYCH 249: Large-Scale Neural Network Modeling for Neuroscience (CS 375)
Introduction to designing, building, and training neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics, memory and attention; integration of variational and generative methods for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models, and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognitive or systems neuroscience.
Terms: Aut
| Units: 3
Instructors:
Yamins, D. (PI)
PSYCH 253: High-Dimensional Methods for Behavioral and Neural Data
Introduction to high-dimensional data analysis and machine learning methods for use in the behavioral and neurosciences, including: supervised methods such as SVMs, linear and nonlinear regression and classifiers, and regularization techniques; statistical methods such as boostrapping, signal detection, factor analysis, and reliability theory; metrics for model/data comparison such as representational similarity analysis; and unsupervised methods such as clustering. Students will learn theory as well as a programming framework for implementing all methods in practice. Prerequisites:
Math 51 or equivalent and
Psych 251 or programming background.
Terms: Spr
| Units: 3
Instructors:
Yamins, D. (PI)
;
Zhuang, C. (PI)
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