Courses tagged with "Information Theory" (117)

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Covers the major results in the study of first language acquisition concentrating on the development of linguistic structure, including sentence structure and morphology. Universal aspects of development are discussed, as well as a variety of cross-linguistic phenomena. Theories of language learning are considered, including parameter-setting and maturation.

Lectures, reading, and discussion of current theory and data concerning the psychology and biology of language acquisition. Emphasizes learning of syntax and morphology, together with some discussion of phonology, and especially research relating grammatical theory and learnability theory to empirical studies of children.

This course is a seminar in real-time language comprehension. It considers models of sentence and discourse comprehension from the linguistic, psychology, and artificial intelligence literature, including symbolic and connectionist models. Topics include ambiguity resolution and linguistic complexity; the use of lexical, syntactic, semantic, pragmatic, contextual and prosodic information in language comprehension; the relationship between the computational resources available in working memory and the language processing mechanism; and the psychological reality of linguistic representations.

This seminar focuses on the cognitive science of moral reasoning. Philosophers debate how we decide which moral actions are permissible. Is it permissible to take one human life in order to save others? We have powerful and surprisingly rich and subtle intuitions to such questions.

In this class, you will learn how intuitions can be studied using formal analytical paradigms and behavioral experiments. Thursday evening, meet to learn about recent advances in theories of moral reasoning. Overnight, formulate a hypothesis about the structure of moral reasoning and design a questionnaire-based experiment to test this. Friday, present and select 1-2 proposals and collect data; we will then reconvene to analyze and discuss results and implications for the structure of the moral mind.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course highlights the interplay between cellular and molecular storage mechanisms and the cognitive neuroscience of memory, with an emphasis on human and animal models of hippocampal mechanisms and function. Class sessions include lectures and discussion of papers.

Surveys general principles and specific examples of motor control in biological systems. Emphasizes the neural mechanisms underlying different aspects of movement and movement planning. Covers sensory reception, reflex arcs, spinal cord organization, pattern generators, muscle function, locomotion, eye movement, and cognitive aspects of motor control. Functions of central motor structures including cerebellum, basal ganglia, and cerebral cortex considered. Cortical plasticity, motor learning and computational approaches to motor control, and motor disorders are discussed.

Roles of neural plasticity in learning and memory and in development of invertebrates and mammals. An in-depth critical analysis of current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Discussion of original papers supplemented by introductory lectures.

Lectures and discussions in this course cover the clinical, behavioral, and molecular aspects of the brain aging processes in humans. Topics include the loss of memory and other cognitive abilities in normal aging, as well as neurodegenerative conditions such as Parkinson's and Alzheimer's diseases. Discussions based on readings taken from primary literature explore the current research in this field.

The neuropharmacology course will discuss the drug-induced changes in functioning of the nervous system. The specific focus of this course will be to provide a description of the cellular and molecular actions of drugs on synaptic transmission. This course will also refer to specific diseases of the nervous system and their treatment in addition to giving an overview of the techniques used for the study of neuropharmacology.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®.

Acknowledgements

The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site.

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Parkinson's disease (PD) is a chronic, progressive, degenerative disease of the brain that produces movement disorders and deficits in executive functions, working memory, visuospatial functions, and internal control of attention. It is named after James Parkinson (1755-1824), the English neurologist who described the first case.

This six-week summer workshop explored different aspects of PD, including clinical characteristics, structural neuroimaging, neuropathology, genetics, and cognitive function (mental status, cognitive control processes, working memory, and long-term declarative memory).  The workshop did not take up the topics of motor control, nondeclarative memory, or treatment. 

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

This class is an introduction to classical mechanics for students who are comfortable with calculus. The main topics are: Vectors, Kinematics, Forces, Motion, Momentum, Energy, Angular Motion, Angular Momentum, Gravity, Planetary Motion, Moving Frames, and the Motion of Rigid Bodies.

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

This course covers central topics in language processing, including: the structure of language; sentence, discourse, and morphological processing; storage and access of words in the mental dictionary; speech processing; the relationship between the computational resources available in working memory and the language processing mechanism; and ambiguity resolution. The course also considers computational modeling, including connectionist models; the relationship between language and thought; and issues in language acquisition including critical period phenomena, the acquisition of speech, and the acquisition of words. Experimental methodologies such as self-paced reading, eye-tracking, cross-modal priming, and neural imaging methods are also examined.

We will examine current research and theory regarding the validity and utility of commonly accepted gender differences in many realms. Topics include: gender differences in cognitive abilities; the social construction of gender; developmental, family, educational and medical influences; and political and economic forces.

To provide instruction and dialog on practical ethical issues relating to the responsible conduct of human and animal research in the brain and cognitive sciences. Specific emphasis will be placed on topics relevant to young researchers including data handling, animal and human subjects, misconduct, mentoring, intellectual property, and publication.