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Tentative Course Offerings

These are tentative schedules. Classes and/or instructors may change or be canceled. Please consult the official Schedule of Classes on TritonLink each quarter.

Featured Courses

Cogs 260: Seminar on Special Topics - Spring 2020

COGS 260 (A00): Development of Executive Cognitive Functions | Professor Gedeon Deak 

"Executive Functions" refer to an amorphous set of cognitive processes that provide humans (and some other animals) with limited volitional control over some cognitive output. Executive functions (EF) affect a wide range of thinking, learning, and behaving, and are reflected in aspects of perceptual-motor, praxis, social, and communicative performance.

Many questions focus on the development of EFs, especially from early childhood through adolescence. How do we acquire the ability to control some of our own cognition? Which EFs develop, and when, and how? What would a biologically and ecologically plausible model of the development of EFs look like?
We will consider these questions with special attention to three intersecting categories of factors: (1) development of the central nervous system; (2) social agents (e.g., schools; parents) and social experiences; and (3) language and semiotic resources or skills.
Students will be expected to attend and participate in discussions, to lead at least one meeting and prepare a supplemental presentation, and to submit an integrative paper or grant proposal at the end of the quarter. Some readings and topics can be tailored to participants' interests (e.g., specific psychiatric or communicative disorders; comparative studies). 
The first meeting will be Thurs. of week 1, 9:00am-11:50pm in CSB 180. Please email with questions.


COGS 260 (B00): Advanced Neural Signal Processing | Professor Eran Mukamel 

Mathematical foundations and applications of neural signal processing techniques used in electrophysiology and neuroimaging (including microscopy), covering spectral analysis, correlation, high-dimensional data, and spike-train analysis.


COGS 260 (C00):Toward a Computational Understanding of Natural Intelligence and Behavior: Models, Algorithms, and Theories | Professor Angela Yu 

The main goal of the class is to foster students' intellectual capacity to think formally about the computational problems that have to be solved by behaving intelligent systems, identify appropriate quantitative tools for modeling such problems and computing their solutions based on empirical data/literature, and designing future scientific experiments to further inform the theory. The course will cover a number of textbook chapters, review papers, and primary research papers, drawing from a variety of quantitative fields: machine learning, reinforcement learning, control theory, information theory, physics, economics, game theory, behavioral ecology, statistics, probability. Note that this is not primarily a methods class, as many relevant methods courses already abound on campus, but rather on scientific applications of these methods.
Pre-req: Cogs 202 or Permission by Instructor
Grading: 40% in-class participation, 40% group presentation, 20% project proposal

Cogs 260: Seminar on Special Topics - Fall 2020

COGS 260 (C00): The 4E’s plus: Embodied, Enactive, Embedded and Extended... | Professor David Kirsh

This course is an exploration of recent rebellions against a classical view of mind that still, in many respects, dominates cognitive science.  The lectures start with a sympathetic statement of the classical view as advanced by Marr, Chomsky, Fodor and Newell and Simon plus a few more recent incarnations.  This is the non-strawman target position. It treats the human cognitive system to be functionally organized with major components solving core informational problems such as seeing or perceiving scenes, identifying what or who is where, understanding and generating language, social cognition, planning and reasoning, predicting future states of the environment, and legions of other functions like facial recognition that we regard as central to being human.  After reviewing this position subsequent lectures describe and evaluate the different E’s – Embodied, Enactive, Embedded, Extended mind and also the common inspiration derived from Situated Cognition and Distributed Cognition. Throughout, we necessarily lean on philosophy insofar as the proponents have usually been philosophers. But our goal is to evaluate the empirical contributions inspired by these metaphysical or methodological positions.  The professor will lecture for half the class, but student participation is central throughout.  

Class meets every Monday 11am-1:50pm online.




COGS 260 (A00): Cognitive Science Graduate Bootcamp | Professor Doug Nitz

For incoming doctoral students in cognitive science. During the first two weeks in September, students commit to ten to fifteen hours per day in lectures and workshops on the history of cognitive science, language and culture, machine learning and neural computation, neuroscience, design and human-computer interaction, programming and statistics, data science, ethnography, and clinical aspects of cognition. Students will attend weekly seminars during fall quarter. S/U grades and 2 units only.

Prerequisites: graduate (PhD) standing only; for students in the following major code CG75.

Cogs 260: Seminar on Special Topics - Spring 2021

COGS 260 (A00): Collective Intelligence | Professor Steven Dow 

How can people and computers work together intelligently to solve complex problems? Collective intelligence is the study of how individuals coordinate, collaborate, and deliberate to produce outcomes greater than any single person do can alone. In this course, students will explore different platforms and mechanisms, discuss ethical and cultural issues, and conduct novel research that contributes to the literature on collective intelligence, crowdsourcing and human computation.  Students will complete homework assignments, discuss key research papers in the field, and create innovative research projects that could lead to publication.   Learn more:

Dsgn 274: Mobile Health Sensing - Spring 2021

DSGN 274: Mobile Health Sensing | Professor Edward Wang 

This class introduces students to the topic of mHeath Sensing. Topical readings curated by the instructor covers topics from machine learning techniques for understanding human emotions through mobile device usage to IoT sensors to monitor flu outbreaks. Lectures introduces methods of design and testing of mHealth sensing solutions, with assignments and final project providing hands-on practicum. To engage effectively, students will need prior familiarity with data processing with Python.

Course Pre-Authorizations

All COGS course pre-authorizations and prerequisite override requests must be made through the UC San Diego Enrollment Authorization System (EASy).