How do words get their meanings? How can word meanings be represented and used by machines? We will explore three families of approaches to these questions from a computational perspective. Relational / structural methods such as semantic networks represent the meaning of words in terms of their relations to other words. Knowledge of the world through perception and action leads to the notion of external grounding, a process by which word meanings are 'attached' to the world. How an agent theorizes about, and conceptualizes its world provides yet another foundation for word meanings. We will examine each of these perspectives, and consider ways to integrate them.

Subject presents a range of advanced topics in integrated logistics and supply chain management. Conducted in a lecture-discussion format, with participation of corporate executives as guest lecturers. Students prepare industry assessment analyses and make formal classroom presentations. Specific topics alternate from year to year, but basic content includes procurement strategies and strategic sourcing, dynamic pricing and revenue management tactics, mitigation of supply chain risk through supply contracts, strategic outsourcing of supply chain chain functions and operations, management and operation of third party logistics providers, and management of supply chain security.

An advanced seminar on issues of current interest in human and machine vision. Topics vary from year to year. Participants discuss current literature as well as their ongoing research.

Survey of structural properties of natural languages, with special emphasis on the sound pattern. Representation of the lexicon. Physiology of speech production, articulatory phonetics. Acoustical theory of speech production; acoustical and articulatory descriptions of phonetic features and of prosodic aspects of speech. Perception of speech. Models of lexical access and of speech production and planning. Applications to recognition and generation of speech by machine, and to the study of speech disorders.

This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

A two-semester course on statistical mechanics. Basic principles are examined in 8.333: the laws of thermodynamics and the concepts of temperature, work, heat, and entropy. Postulates of classical statistical mechanics, microcanonical, canonical, and grand canonical distributions; applications to lattice vibrations, ideal gas, photon gas. Quantum statistical mechanics; Fermi and Bose systems. Interacting systems: cluster expansions, van der Waal's gas, and mean-field theory. Topics from modern statistical mechanics are explored in 8.334: the hydrodynamic limit and classical field theories. Phase transitions and broken symmetries: universality, correlation functions, and scaling theory. The renormalization approach to collective phenomena. Dynamic critical behavior. Random systems.

Statistical Mechanics is a probabilistic approach to equilibrium properties of large numbers of degrees of freedom. In this two-semester course, basic principles are examined. Topics include: thermodynamics, probability theory, kinetic theory, classical statistical mechanics, interacting systems, quantum statistical mechanics, and identical particles.

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Statistical Reasoning in Public Health II provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. The course builds on the material in Statistical Reasoning in Public Health I , extending the statistical procedures discussed in that course to the multivariate realm, via multiple regression methods. New topics, such as methods for clinical diagnostic testing, and univariate, bivariate, and multivariate techniques for survival analysis will also be covered. These topics will be reinforced with many "real-life" examples drawn from recent biomedical literature. While there are some formulae and computational elements to the course, the emphasis is again on interpretation and concepts.

This course provides an introduction to the physical chemistry of biological systems. Topics include: connection of macroscopic thermodynamic properties to microscopic molecular properties using statistical mechanics, chemical potentials, equilibrium states, binding cooperativity, behavior of macromolecules in solution and at interfaces, and solvation. Example problems include protein structure, genomic analysis, single molecule biomechanics, and biomaterials.

This course explores the theory of self-assembly in surfactant-water (micellar) and surfactant-water-oil (micro-emulsion) systems. It also introduces the theory of polymer solutions, as well as scattering techniques, light, x-ray, and neutron scattering applied to studies of the structure and dynamics of complex liquids, and modern theory of the liquid state relevant to structured (supramolecular) liquids.

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Fundamentals of detection and estimation for signal processing, communications, and control. Vector spaces of random variables. Bayesian and Neyman-Pearson hypothesis testing. Bayesian and nonrandom parameter estimation. Minimum-variance unbiased estimators and the Cramer-Rao bounds. Representations for stochastic processes; shaping and whitening filters; Karhunen-Loeve expansions. Detection and estimation from waveform observations. Advanced topics: linear prediction and spectral estimation; Wiener and Kalman filters.

12.103 explores the role of scientific knowledge, discovery, method, and argument in environmental policymaking from both idealistic and realistic perspectives. The course will use case studies of science-intensive environmental controversies to study how science was used and abused in the policymaking process. Case studies include: global warming, biodiversity loss, and nuclear waste disposal siting. Subject includes intensive practice in the writing and presentation of "position statements" on environmental science issues.

" This is a one-semester class about gauge/gravity duality (often called AdS/CFT) and its applications."

Introduction to the main concepts of string theory to undergraduates. Since string theory is quantum mechanics of a relativistic string, the foundations of the subject can be explained to students exposed to both special relativity (8.033) and basic quantum mechanics (8.05). Subject develops the aspects of string theory and makes it accessible to students familiar with basic electromagnetism (8.02) and statistical mechanics (8.044). This includes the study of D-branes and string thermodynamics. This course introduces string theory to undergraduate and is based upon Prof. Zwiebach's textbook entitled A First Course in String Theory. Since string theory is quantum mechanics of a relativistic string, the foundations of the subject can be explained to students exposed to both special relativity and basic quantum mechanics. This course develops the aspects of string theory and makes it accessible to students familiar with basic electromagnetism and statistical mechanics.

The strong force which bind quarks together is described by a relativistic quantum field theory called quantum chromodynamics (QCD). Subject surveys: The QCD Langrangian, asymptotic freedom and deep inelastic scattering, jets, the QCD vacuum, instantons and the U(1) problem, lattice guage theory, and other phases of QCD.

The strong force which bind quarks together is described by a relativistic quantum field theory called quantum chromodynamics (QCD). Subject surveys: The QCD Langrangian, asymptotic freedom and deep inelastic scattering, jets, the QCD vacuum, instantons and the U(1) problem, lattice guage theory, and other phases of QCD. Strong Interactions is a course in the construction and application of effective field theories, which are a modern tool of choice in making predictions based on the Standard Model. Concepts such as matching, renormalization, the operator product expansion, power counting, and running with the renormalization group will be discussed. Topics will be taken from heavy quark decays and CP violation, factorization in hard processes (deep inelastic scattering and exclusive processes), non-relativistic bound states in field theory (QED and QCD), chiral perturbation theory, few-nucleon systems, and possibly other Standard Model subjects.