This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations.

This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations.

This course is to fulfill the work plan agreed by the student and the dissertation adviser. The guiding principle of the Dissertation Research course is to complete the doctoral dissertation research proposed by the doctoral candidate and approved by the candidate’s dissertation committee. The course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the dissertation document for partial fulfillment of the requirements of the PhD degree in Cognitive Science.

This course provides the graduate student an opportunity to explore an aspect of data science independently and in depth, under the direction of an advisor. The student selects a topic and then works with a faculty member to describe the value of the work and the deliverables.

This course covers the underlying theories and algorithms used in the field of artificial intelligence. Topics include the history of AI, search algorithms (such as A*, game search and constraint satisfaction), logic and logic programming, planning, and an overview of machine learning. Programming assignments, including implementation of AI algorithms, and oral/written summaries of research papers are required.

This course introduces the mathematical background necessary to understand, design, and effectively deploy AI systems. It focuses on four key areas of mathematics: (1) linear algebra, which enables describing, storing, analyzing and manipulating large-scale data; (2) optimization theory, which provides a framework for training AI systems; (3) probability and statistics, which underpin many machine learning algorithms and systems; and (4) numerical analysis, which illuminates the behavior of mathematical and statistical algorithms when implemented on computers.

This course will familiarize students with foundational concepts and emerging ideas in the ethics of artificial intelligence and their implications for public policy. It will be broken down into three sections: (1) the ethics of machine learning; (2) the moral status of AI; and (3) AI and the distant future. The first section will consider such topics as the ethical implications of unconscious bias in machine learning (e.g., in predictive text, facial recognition, speech dialogue systems); what constraints should govern the behavior of autonomous and semi-autonomous machines such as drones and smart cars; whether AI can undermine valuable social institutions and perhaps to democracy itself and what might be done to mitigate such risk; and how automation might transform the labor economy and whether this morally desirable. The second section turns to the question of our moral obligations toward (some) artificial intelligences. Here, we will ask what grounds moral status in general and how this might apply to artificial intelligences in particular, including how should we should balance moral obligations toward (some) AIs with competing obligations toward human beings and other creatures with morally protectable interests. The final section will look to the far distant future and consider how (if at all) we might identify and estimate future threats from AI and what might be done today to protect all those who matter morally.

This course is an introduction to machine learning theories and algorithms. Topics include an overview of data collection, sampling and visualization techniques, supervised and unsupervised learning and graphical models. Specific techniques that may be covered include classification (e.g., support vector machines, tree-based models, neural networks), regression, model selection and some deep learning techniques. Programming assignments and oral/written summaries of research papers are required.

Hallmarks of AI are systems that perform human-like behaviors, and AI systems rely on continuous preparation and deployment of data resources as new tasks emerge. In this course, students develop their conceptual, applied, and critical understanding about (1) experimental principles and methods guiding the collection, validation, and deployment of human data resources for AI systems; (2) human-centered AI concepts and techniques including dataset bias, debiasing, AI fairness, humans-in-the loop methods, explainable AI, trust), and (3) best practices for technical writing and presentation about AI. As a milestone, based on research review, students will write and present an experimental design proposal for dataset elicitation followed by computational experimentation, with description and visualization of the intended experiment setup, as well as critical reflection of benefits, limitations, and implications in the context of AI system development and deployment.

Graduate capstone project by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Machine learning, including standard and recent neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools. Expected: Programming skills, demonstrated by coursework or instructor approval.

The Thesis courses will vary widely but will fulfill the work plan agreed by the student and the adviser. The guiding principles of the Thesis Proposal course are to initiate thesis research including selecting a thesis advisor, choosing and defining a topic, surveying relevant research literature, and planning the research. To complete the course, the student will successfully submit and defend a thesis proposal, which is a detailed and complete plan of the thesis research. The thesis proposal should include exhaustive review of relevant literature, statement of the student's thesis, formulation of hypotheses, operational definitions of independent and dependent variables, and a detailed procedure for carrying out the research. The proposal may also include a section on anticipated results with a detailed plan for analysis of data.