Program in Biomedical Informatics
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Prereq.: 7.012 or 7.013 or 7.014, 7.05
Units: 4-0-8
Focuses on the scientific, clinical, and ethical aspects of
human genetics. Basic science lectures covering molecular genetics are integrated
with patient presentations and discussion. An outside project puts each student
in direct contact with clinicians, researchers, and patients. During the first
part of the class, background for this and other basic science subjects is
introduced, while students with stronger backgrounds meet in alternative
sections to discuss related advance topics based on reading primary literature.
(Only HST students may register under HST.160, graded P/D/F.)
D. Housman, N. Rosenthal
Prereq.: 7.012 or 7.013 or 7.014, 7.05
Units: 2-0-4
Introduction to central issues in medical genetics.
Significance of karyotypic analysis in clinical genetics and oncology. In-depth
consideration of well-defined, genetically based illnesses including cystic
fibrosis, muscular dystrophies, and Huntington's disease. Clinical issues posed
by predisposition to common forms of illness such as diabetes, atherosclerosis,
and specific forms of cancer addressed from a molecular genetic perspective.
Includes patient presentations, consideration of genetic counseling issues, and
the likely clinical impact of new genetic diagnostic techniques. (Only HST
students may register under HST.180, graded P/D/F.)
B. Korf
Prereq.: Enrollment limited, open only to medical and
graduate students, 18.02
Units: 3-0-3
Introduces statistical logic and technique as a basis for
clinical decisions and scientific inference. Students learn to perform
elementary statistical calculations, use a statistics computer program (STATA),
and acquire the concepts and vocabulary to read biomedical literature
critically and communicate productively with statistical professionals.
Includes probability theory, normal sampling, chi-square and t-tests, analysis
of variance, linear regression, and survival analysis. Case studies include
applications to diagnostic screening, clinical drug trials, and physiological
experiments. Emphasis on experimental studies rather than epidemiology. (Only
HST students may register under HST.190, graded P/D/F.)
D. Finkelstein
Prereq.: Basic understanding of molecular biology,
statistics, and computers
Units arranged
Recitation: TBA
(HARVARD)
Subject assesses the relationships between sequence,
structure, and function in complex biological networks as well as progress in
realistic modeling of quantitative, comprehensive functional-genomics analyses.
Topics include: algorithmic, statistical, database, and simulation approaches;
and practical applications to biotechnology, drug discovery, and genetic
engineering. Future opportunities and current limitations critically assessed.
Problem sets and project emphasize creative, hands-on analyses using these
concepts.
G. Church
Information
Technology in the Healthcare System of the Future
MIT
Units: 2-3-7
Instructors:
S. E. Locke, B. P. Bergeron, J. Blander
Prerequisite:
Concomitant registration in HST 921/922 required except by permission of
Instructor
Offered:
G (Spring) - Time: Th 3:00 - 7:00
Place:
HMS MEC 250
Student
labs provide a survey of emerging information technologies as used in
healthcare. Stakeholder and market analysis techniques are used to examine the following: voice recognition,
palm computing, wireless networks, patient kiosks, bedside expert systems,
healthcare e-commerce, and clinical
trials. Students in medicine, business, law, engineering, computer science, media, public health, and
government design an innovative
information technology solution to a current or future health care
problem. Design projects presented
during the final class. (Only HST students may
register under HST.923, graded P/D/F.)
(Same subject as 10.555J)
Prereq.: Permission of instructor
Units: 3-0-6
Introduction to bioinformatics, the collection of principles
and computational methods used to upgrade the information content of biological
data generated by genome sequencing, proteomics, and cell-wide physiological
measurements of gene expression and metabolic fluxes. Fundamentals from systems
theory presented to define modeling philosophies and simulation methodologies
for the integration of genomic and physiological data in the analysis of
complex biological processes. Various computational methods address a broad
spectrum of problems in functional genomics and cell physiology. Application of
bioinformatics to metabolic engineering, drug design, and biotechnology also
discussed.
Geo. Stephanopoulos, I. Rigoutsos,
Gr. Stephanopoulos
(Subject meets with 6.034)
Prereq.: 6.001
Units: 5-3-4
Lecture: MW9
(10-250) Recitation: R2 (34-303) or R3 (34-303) or R4 (34-303) or F9 (34-303)
or F10 (34-302) or F11 (26-322) or F11 (36-372) or F12 (24-407)
See description under subject 6.034.
P. Szolovits
(Same subject as 6.872J)
Prereq.: 6.034
Units: 3-0-9
URL: http://www.chip.org/chip/courses/1999.6.872/6.872.1999.html
See description under subject 6.872J.
P. Szolovits, I. Kohane, L. Ohno-Machado
(Same subject as 6.873J)
Prereq.: 6.034 or HST.947; programming skills or permission
of instructor
Units: 3-0-9
URL: http://dsg.harvard.edu/courses/hst951/
Presents the main concepts of decision analysis, artificial
intelligence, and predictive model construction and evaluation in the specific
context of medical applications. Emphasizes the advantages and disadvantages of
using these methods in real-world systems and provides hands-on experience.
Technical focus on decision analysis, knowledge-based systems (qualitative and
quantitative), learning systems (including logistic regression, classification
trees, neural networks), and techniques to evaluate the performance of such
systems. Students produce a final project using the methods learned in the
subject, based on actual clinical data. (Required for students in the Master's
Program in Medical Informatics, but open to other graduate students and
advanced undergraduates.)
L. Ohno-Machado, I. Kohane, P. Szolovits
Computing
for Biomedical Scientists
MIT
units: 3-0-9
Instructors:
O. Ogunyemi, A. Boxwala, Q. Zeng
Prerequisite:
Graduate level biomedical background or
permission of instructors
Introduces abstraction as an important mechanism for
problem decomposition and solution formulation in the biomedical domain, and
examines computer representation, storage, retrieval, and manipulation of
biomedical data. Examines effect of
programming paradigm choice on problem-solving approaches, introduces data
structures and algorithms. Presents
knowledge representation schemes for capturing biomedical domain complexity.
Teaches principles of data modeling for efficient storage and retrieval. The
final project involves building a medical information system that encompasses
the different concepts taught in the course.
Prereq.: --
Units arranged [P/D/F]
Recitation: TBA
Research methods and ideas involved in addressing the
information needs of medical education, medical practice, and biomedical
research. Topics include clinical information system design, medical knowledge
representation, clinical decision making, cost effectiveness analysis, image
management, software engineering, and evaluation approaches for information
systems. Activities in various research groups are analyzed, and supplemented
by readings and discussions. A written proposal and supervised project work are
required.
R. A. Greenes, P. Szolovits, G. O. Barnett, S. G. Pauker, I.
Kohane, C. Safran
Prereq.: 6.003, 6.041
Units: 4-0-8
Lecture: MW11
(34-101) Recitation: TR11 (34-301) or TR12 (34-301) or TR1 (34-301) or TR2
(34-301) +final
Input-output and state-space models of linear systems driven
by deterministic and random signals; time- and transform-domain
representations. Sampling, discrete-time processing of continuous-time signals.
State feedback and observers. Probabilistic models; stochastic processes,
correlation functions, power spectra, and whitening filters. Detection; matched
filters. Least-mean square error estimation; Wiener filtering.
A. V. Oppenheim, G. C. Verghese
(Subject meets with HST.947)
Prereq.: 6.001
Units: 5-3-4
Lecture: MW9
(10-250) Recitation: R2 (34-303) or R3 (34-303) or R4 (34-303) or F9 (34-303)
or F10 (34-302) or F11 (26-322) or F11 (36-372) or F12 (24-407)
Introduces representations, techniques, and architectures
used to build applied systems and to account for intelligence from a
computational point of view. Applications of rule chaining, heuristic search,
constraint propagation, constrained search, inheritance, and other
problem-solving paradigms. Applications of identification trees, neural nets,
genetic algorithms, and other learning paradigms. Speculations on the
contributions of human vision and language systems to human intelligence. Enrollment
may be limited.
P. H. Winston
(Subject meets with 6.041)
Prereq.: 18.02
Units: 4-0-8
URL: http://web.mit.edu/6.041/www/home.html
Lecture: WF12
(34-101) +final
Meets with undergraduate subject 6.041. Requires the completion
of additional advanced home problems. See description under subject 6.041.
D. P. Bertsekas, J. N. Tsitsiklis
Prereq.: 6.011; 18.06
Units: 4-0-8
URL: http://web.mit.edu/6.432/www/
Lecture: TR9:30-11
(2-105) Recitation: R2 (2-132) or F10 (2-136) or F11 (2-136) +final
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.
A. S. Willsky, G. W. Wornell
Prereq.: 6.241, 6.432
Units: 3-0-9
Mathematical models of systems from observations of their
behavior. Time series, state-space, and input-output models. Model structures,
parametrization, and identifiability. Non-parametric methods. Prediction error
methods for parameter estimation, convergence, consistency, andasymptotic
distribution. Relations to maximum likelihood estimation. Recursive estimation;
relation to Kalman filters; structure determination; order estimation; Akaike
criterion; and bounded but unknown noise models. Robustness and practical
issues. Alternate years.
M. A. Dahleh, B. C. Lesieutre, S. K. Mitter
Prereq.: 6.401 or 6.262 or 6.432
Units: 3-0-9
Introduction to the quantitative theory of information and
its applications to reliable, efficient communication systems. Mathematical
definition and properties of information. The source coding theorem. Lossless
compression of data, including adaptive compression for unknown source
statistics. Noisy communication channels, the data processing theorem, and
fundamental limits on decoding error. Introduction to algebraic and
convolutional error correction coding techniques.
A. Lapidoth
(Same subject as STS.085J)
Prereq.: --
Units: 3-0-9
Studies the growth of computer and communications technology
and the new legal and ethical challenges that reflect tensions between
individual rights and societal needs. Topics: computer crime; intellectual
property restrictions on software; encryption, privacy, and national security;
academic freedom and free speech. Students meet and question technologists,
activists, law enforcement agents, journalists, and legal experts. Extensive
use of World Wide Web for readings and other materials. Enrollment limited.
H. Abelson, M. Fischer
Prereq.: 6.042J (6.046J and 6.034 desirable) or equivalent
Units: 3-0-9
Lecture: TR9:30-11 (34-302)
A graduate-level introduction to artificial intelligence.
Topics include: representation and inference in first-order logic; modern
deterministic and decision-theoretic planning techniques; basic supervised
learning methods; and Bayesian network inference and learning.
L. Kaelbling
(Subject meets with 6.803)
Prereq.: 6.034
Units: 3-0-9
Analyzes seminal work directed at the development of a
computational understanding of human intelligence, such as work on object
tracking, object recognition, change representation, language evolution, and
the role of symbols in learning and communication. Reviews visionary ideas of
Turing, Minsky, and other influential thinkers. Examines the role of brain
scanning, systems neuroscience, and cognitive psychology. Emphasis on
discussion and analysis of original papers. Meets with graduate subject 6.833
but assignments differ.
P. H. Winston
Prereq.: 6.034, 18.03, 18.06, permission of instructor
Units: 3-0-9
URL: http://www.ai.mit.edu/courses/6.836/
Studies how to build intelligent systems that have physical
embodiment. Examines specific problems, historical solutions, and contemporary
research into the area of autonomous embodied systems. Topics: dynamical
modeling of agent/environment interaction; neural modeling of perception and
action systems; issues in vision and robotics; evolutionary modeling
techniques; behavior-based approaches; and pre-cognitive and cognitive
architectures. Examines problems and sources of simplification presented by a
physically embodied system relative to unembodied intelligence.
R. A. Brooks
(Same subject as 9.611J)
Prereq.: 6.034
Units: 3-3-6
Relationship between computer representation of knowledge
and the structure of natural language. Emphasizes development of the analytical
skills necessary to judge the computational implications of grammatical
formalisms, and uses concrete examples to illustrate particular computational
issues. Efficient parsing algorithms for context-free grammars; augmented
transition network grammars. Question answering systems. Extensive laboratory
work on building natural language processing systems. 8 Engineering Design
Points.
R. C. Berwick
Prereq.: 6.034, 18.06, 6.041 or 18.05
Units: 3-0-9
Lecture: TR2:30-4 (34-302)
Techniques and algorithms in machine learning; statistical
inference as a foundation for these methods; simple perceptrons; boosting;
support vector machines; hidden Markov models; and Bayesian networks.
T. Jaakkola
(Same subject as MAS.731J)
Prereq.: Must have read The Society of Mind, permission of
instructor
Units: 2-0-10
URL: http://www.media.mit.edu/people/minsky/6868/
Introduction to a theory that tries to explain how minds are
made from collections of simpler processes. Treats such aspects of thinking as
vision, language, learning, reasoning, memory, consciousness, ideals, emotions,
and personality. Incorporates ideas from psychology, artificial intelligence,
and computer science to resolve theoretical issues such as wholes vs parts, structural
vs functional descriptions, declarative vs procedural representations, symbolic
vs connectionist models, and logical vs common-sense theories of learning.
Enrollment limited.
M. Minsky
Prereq.: 6.034
Units: 3-0-9
URL: http://www.ai.mit.edu/courses/6.871/
Development of programs containing a significant amount of
knowledge about their application domain. Outline: brief review of relevant AI
techniques; case studies from a number of application domains, chosen to
illustrate principles of system development; discussion of technical issues
encountered in building a system, including selection of knowledge
representation, knowledge acquisition, etc.; and discussion of current and
future research. Hands-on experience in building an expert system (term
project). 8 Engineering Design Points.
R. Davis, H. E. Shrobe
(Same subject as HST.950J)
Prereq.: 6.034
Units: 3-0-9
URL:
http://www.chip.org/chip/courses/1999.6.872/6.872.1999.html
Analyzes computational needs of clinical medicine, reviews
systems and approaches that have been used to support those needs, and examines
new technologies. Topics: the nature of clinical data; architecture and design
of healthcare information systems; privacy and security issues; medical expert
systems; and computing support for medical education. Case studies of
contemporary systems. Term project using a large pseudonymized clinical dataset
integrates classroom topics. 6 Engineering Design Points.
P. Szolovits, I. Kohane, L. Ohno-Machado
(Same subject as HST.951J)
Prereq.: 6.034 or HST.947; programming skills or permission
of instructor
Units: 3-0-9
Presents the main concepts of decision analysis, artificial
intelligence, and predictive model construction and evaluation in the specific
context of medical applications. Emphasizes the advantages and disadvantages of
using these methods in real-world systems and provides hands-on experience.
Technical focus on decision analysis, knowledge-based systems (qualitative and
quantitative), learning systems (including logistic regression, classification
trees, neural networks), and techniques to evaluate the performance of such
systems. Students produce a final project using the methods learned in the
subject, based on actual clinical data. (Required for students in the Master's
Program in Medical Informatics, but open to other graduate students and
advanced undergraduates.)
L. Ohno-Machado, I. Kohane, P. Szolovits
Prereq.: Permission of instructor
Units: 3-0-9 [P/D/F]
Applied statistics covers probability and distributions
(normal binomial, poisson, exponential, lognormal, and uniform), estimation and
hypothesis testing, parametric and non-parametric one-sample and two-sample
tests of means, analysis of variance for crossed and nested designs, linar and
multiple regression with residual analysis, correlation and discrete data
analysis using chi-squared tests. Discussion of experimental and sampling
designs are included. Examples use data from biological studies.
Starczak
Prereq.: Permission of instructor
Units: 4-0-8
Lecture:
TR9:30-11:30 (56-154)
Principles and approaches of genetic analysis, including Mendelian
systems and prokaryotic genetics. Application of principles to biological
function, including regulation and development. Mechanisms of recombination,
mutation, and evolution. Review of problem sets and exams supplement lectures.
H. R. Horvitz, T. Orr-Weaver
(Subject meets with 7.28)
Prereq.: 7.03; 7.05
Units: 5-0-7
Detailed analysis of the biochemical mechanisms that control
the maintenance, expression, and evolution of prokaryotic and eukaryotic
genomes. Topics covered in lecture and readings of relevant literature include:
gene regulation, DNA replication, genetic recombination, and translation. Logic
of experimental design and data analysis are emphasized. Presentations include
both lectures and group discussions of representative papers from the
literature. Graduate students are expected to explore the subject in greater
depth.
S. Bell, T. Baker
Prereq.: 7.28 or permission of instructor
Units: 3-0-9
Study and discussion of computational approaches and
algorithms for contemporary problems in functional genomics. Topics include DNA
chip design, experimental data normalization, expression data representation
standards, proteomics, gene clustering, self-organizing maps, Boolean networks,
statistical graph models, Bayesian network models, continuous dynamic models,
statistical metrics for model validation, model elaboration, experiment
planning, and the computational complexity of functional genomics problems.
R. Young, D. Gifford, T. Jaakkola
Prereq.: 18.03 and 8.02 or permission of instructor
Units: 3-0-9
Computation in the brain as the interplay between coding and
dynamics. Mathematical introduction to the biophysics of neurons and the
emergent properties of networks, with applications to sensory transduction,
visual and auditory perception, motor control, language, cognition, and
learning and memory. Comparison of the brain to the hardware and software of
engineered computational systems.
H. S. Seung
(Subject meets with 9.343J, MAS.234J, MAS.654)
Prereq.: 9.00 or permission of instructor
Units: 3-0-6
Lecture: TR1:30-3
(NE20-461)
The acquisition and communication of knowledge demands a
coherent cognitive framework within which we can reason about events and states
in the world. What frameworks are plausible, and how do these choices affect
our deductive and creative processes? Material includes analog representations,
Bayesian nets, grammars, default logics, belief theory, and discourse analysis.
W. A. Richards
Prereq.: 18.02, 9.641, 6.893 or permission of instructor
Units: 3-0-9
URL:
http://www.ai.mit.edu/projects/cbcl/courses/course9.520/
Focuses on the problem of supervised learning from the
perspective of statistics and of the theory of multivariate function
approximation from sparse data. Includes topics such as VC theory,
Regularization, Support Vector Machines for regression and classification and
advanced topics such as boosting, feature selection and active learning.
Examines applications in areas such as computer vision, computer graphics and
time-series analysis and prediction. Also considers implications for how the
brain may learn from experience, focusing on the neurobiology of object
recognition. A significant increase in hands-on applications and exercises is
planned, paralleling the rapidly increasing practical uses of the techniques
described in the subject.
T. Poggio, A. Verri
Prereq.: 9.29 or permission of instructor
Units: 3-0-9
URL: http://hebb.mit.edu/courses/9.641/
Organization of synaptic connectivity as the basis of neural
computation and learning. Single and multilayer perceptrons. Dynamical theories
of recurrent networks: amplifiers, attractors, and hybrid computation.
Backpropagation and Hebbian learning. Models of perception, motor control,
memory, and neural development. Alternate years.
H. S. Seung
(Same subject as
HST.903J)
Prereq.: 14.04, permission of instructor
Units: 3-0-9
Advanced subject in economics of health care sector.
Considers selected topics in depth, such as design and financing of health
insurance, behavior of nonprofit hospitals, role of competition in the medical
care market, determinants of technological change, and effects of government
regulations.
J. E. Harris
Prereq.: 14.30
Units: 4-0-8
Lecture: TR2:30-4
(E51-151) Recitation: F3 (E51-151)
Introduction to econometric models and techniques,
emphasizing regression. Advanced topics include instrumental variables, panel
data methods, measurement error, and limited dependent variable models.
Includes problem sets. May not count toward HASS requirement.
Fall Term: W. Newey
Spring Term: J. Voth
Prereq.: 18.02,
permission of instructor
Units: 4-0-8
Lecture: TR9-10:30
(E51-151) Recitation: F9-10:30 (E51-361) +final
Self-contained introduction to probability and statistics as
background for advanced econometrics. Elements of probability theory; sampling
theory; asymptotic approximations; decision-theory approach to statistical estimation
focusing on regression, hypothesis testing; and maximum-likelihood methods.
Illustrations from economics and application of these concepts to economic
problems. Class size limited.
G. Kuersteiner
Prereq.: 14.381 or
permission of instructor
Units: 4-0-8
Specification and estimation of the linear regression model.
Departures from the standard Gauss-Markov assumptions include
heteroskedasticity, serial correlation, and errors in variables. Advanced
topics include generalized least squares, instrumental variables, nonlinear
regression, and limited dependent variable models. Economic applications are
discussed. Class size limited.
V. Chernozhukov, J. Hausman
Prereq.: 14.382, permission of instructor
Units: 4-0-8
FIRST HALF TERM ONLY
Covers identification and estimation of linear and nonlinear
simultaneous equations models. Requires econometrics paper due at the end of
IAP. Class size limited.
J. Hausman
Prereq.: 14.382 or permission of instructor
Units: 2-0-4
SECOND HALF TERM
Theory and application of time series methods in
econometrics, including representation theorems, decomposition theorems,
prediction, spectral analysis, estimation with stationary and nonstationary processes,
VARs, unit roots, and cointegration.
G. Kuersteiner
Prereq.: 14.382 or
permission of instructor
Units: 2-0-4
SECOND HALF TERM
Micro-econometric models, including large sample theory for estimation and hypothesis
testing, generalized method of moments, estimation of censored and truncated
specifications and duration models, nonparametric and semiparametric
estimation, panel data, bootstrapping, and simulation methods. Methods
illustrated with economic applications.
W. Newey
Prereq.: 14.383
Units: 4-0-8
Focuses on recent developments in econometrics. Topics
include empirical processes and asymptotic theory, nonparametric and
semiparametric estimation, estimation of auction and other structural models,
unit roots and cointegration, and continuous time econometrics. Results
illustrated with economic applications.
W. Newey
Prereq.: 18.06 or permission of instructor
Units: 4-0-8
You must pre-register and participate in Sloan's
Prioritization process to take this subject.
Introduces students to the theory, algorithms, and
applications of optimization. The optimization methodologies include linear
programming, network optimization, dynamic programming, integer programming,
non-linear programming, and heuristics. Applications to logistics,
manufacturing, transportation, E-commerce, project management, and finance.
J. B. Orlin
Prereq.: Permission of instructor
Units: 3-0-6
Application-oriented introduction to systems optimization
focusing on understanding system tradeoffs. Introduces modeling methodology
(linear, network, integer, nonlinear programming, and heuristics), modeling
tools (sensitivity and postoptimality analysis), software, and applications in
production planning and scheduling, inventory planning, supply network
optimization, project scheduling, telecommunications, facility sizing and
capacity expansion, product development, yield management, electronic trading,
and finance.
A. S. Schulz
Prereq.: --
Units: 3-0-6
You must
pre-register and participate in Sloan's Prioritization process to take this subject.
RESTRICTED TO 1ST
YEAR MASTERS FIRST HALF TERM ONLY
Introduces students to the basic tools in using data to make
informed management decisions. Covers introductory probability, decision
analysis, basic statistics, regression, simulation, linear and nonlinear
optimization, and discrete optimization. Computer spreadsheet exercises, cases,
and examples drawn from marketing, finance, operations management, and other
management functions. Restricted to first-year Sloan master's students.
R. M. Freund, G. Perakis, D. Bertsimas
Prereq.: 15.060 or
equivalent
Units: 2-0-4
Introduces students to a class of methods known as data
mining that assists managers in recognizing patterns and making intelligent use
of massive amounts of electronic data collected via the Internet, e-commerce,
electronic banking, point-of-sale devices, bar-code readers, and intelligent
machines. Topics covered: subset selection in regression, collaborative
filtering, tree-structured classification and regression, cluster analysis, and
neural network methods. Examples of successful applications in areas such as
credit ratings, fraud detection, database marketing, customer relationship
management, and investments and logistics are covered. Hands-on experimentation
with data-mining software is used.
D. Bertsimas, N. Patel
Prereq.: --
Units: 3-0-6
Introduces students to the basic tools in using data to make
informed management decisions. Covers introductory probability, decision
analysis, basic statistics, regression, simulation, and linear and nonlinear
optimization. Computer spreadsheet exercises and examples drawn from marketing,
finance, operations management, and other management functions. Restricted to
Sloan Fellows.
Consult S. J. Sacca.
Prereq.: 15.061 or equivalent
Units: 2-0-4
Follow-on subject to 15.061. Applied treatment of analysis
of variance, nonparametric methods, forecasting, and ``reductionist'' techniques
like factor analysis.
A. I. Barnett
Prereq.: 6.262, 18.100, or equivalent
Units: 3-0-9
Stochastic analysis and modeling. Topics include measure
theoretic probability, martingales, optional sampling, diffusion processes and
stochastic integration, with a strong emphasis on analysis of Brownian motion,
and efficient simulation. Examples from several problem areas, including
manufacturing, telecommunications, finance, and electrical engineering, are
discussed to illustrate and motivate the mathematical concepts. Alternate
years.
Y. Wang, D. J. Bertsimas
Prereq.: 15.060
Units: 2-0-4
Subject develops and illustrates modeling tools for working
with data and making effective decisions based on such models and data-driven
analysis, continuing from the material covered in subject 15.060. Topics
include hypothesis testing, pitfalls of casually presumptive analysis, more
coverage of regression models, plus supply chain modeling, revenue management
models, optimization under uncertainty, and dynamic optimization and pricing.
Restricted to Sloan master's students. Half term subject.
A. Barnett, R. Freund
Prereq.: 6.041 or 18.440, 18.06
Units: 4-0-8
You must
pre-register and participate in Sloan's Prioritization process to take this
subject.
Lecture: MW2:30-4
(E51-376) Recitation: T4 (E51-335) +final
Introduces statistical data analysis, concentrating on
techniques used in management science and finance. Topics chosen from:
statistical graphics, basics of sampling, estimation, hypothesis testing,
linear and logistic regression, analysis of variance, contingency tables,
forecasting, statistical quality control, principal components, and factor
analysis. SAS or similar package used for data analysis.
R. E. Welsch, G. M. Kaufman
Prereq.: 6.431 or 18.440, 18.06 or 18.700
Units: 2-0-4
Introduction to statistical theory and methodology,
concentrating on techniques used in finance, marketing, and operations
management. Primarily for Ph.D. and M.S. students with good backgrounds in
probability and matrix algebra. Topics: sampling, theory of estimation,
testing, nonparametric statistics, analysis of variance, and regression
analysis. Students should consider 15.077, 15.036, or 14.382 after completion
of this subject. SAS, SPLUS, or similar package used for data analysis. Subject
offered first half of term.
R. E. Welsch
Prereq.: 15.076 or 14.381 or 14.30 or 15.064J, 18.06 or
18.700
Units: 2-0-4
Introduction to modern regression, analysis of variance, and
multivariate analysis, concentrating on methods most often used in finance,
marketing, and operations management. Topics selected from: multiple and
multivariate regression, logistic regression, higher-way analysis of variance,
discrete multivariate analysis, factor analysis, principal components,
discriminant analysis, multivariate process control, partial least squares, and
nonparametric regression MARS. SAS, SPLUS, or similar package used for data
analysis. Subject offered second half of term.
R. E. Welsch
Prereq.: 18.06, 15.075 or 18.441 or 18.443
Units: 3-0-9
Theory and application of commonly used techniques involving
multivariate data. Attention devoted to specific applications, and to
computational facilities for applying the methods. Selects topics from the
following: multivariate regression, discriminate analysis, and pattern
classification. Cluster analysis, factor analysis, and principal components.
Multidimensional scale analysis. Contingency tables.
Information: G. M. Kaufman.
(Same subject as 6.859J)
Prereq.: 15.081J or permission of instructor
Units: 3-0-9
Devoted to an in-depth treatment of important and modern
topics in combinatorial optimization and integer programming. Topics in
combinatorial optimization include computational complexity, matroid theory,
matching theory, polyhedral combinatorics, Lipschitz embeddings and
multicommodity flow, approximation algorithms, and local search. Topics in
integer programming include Diophantine equations, Hermite's normal form,
unimodular matrices, basis reduction, Groebner bases, test sets, Hilbert bases,
Lagrangean relaxation, column generation, and branch-and-bound. Alternate
years.
D. J. Bertsimas
(Same subject as 2.098J)
Prereq.: 18.06 or equivalent
Units: 3-0-9
You must
pre-register and participate in Sloan's Prioritization process to take this
subject.
Lecture: TR8-9:30
(1-390) +final
Subject introduces the principal algorithms for linear,
network, discrete, nonlinear, dynamic optimization and optimal control.
Emphasis on methodology and the underlying mathematical structures. Topics
include the simplex method, network flow methods, branch and bound and cutting
plane methods for discrete optimization, optimality conditions for nonlinear
optimization, interior point methods for convex optimization, Newton's method,
heuristic methods, and dynamic programming and optimal control methods.
D. Bertsimas, R. M. Freund
Prereq.: 15.093 or equivalent
Units: 3-0-9
An application-oriented introduction to the modeling of
large-scale systems in a wide variety of decision-making domains and the
optimization of such systems using state-of-the-art optimization software.
Application domains include transportation and logistics, manufacturing and other
system scheduling, pattern classification, structural design, financial
engineering, and telecommunications system planning. Modeling tools and
techniques covered include linear, network, discrete, and nonlinear
programming, heuristic methods, sensitivity and postoptimality analysis,
decomposition methods for large-scale systems, and stochastic programming.
R. M. Freund
(Same subject as HST.918J)
Prereq.: Permission of instructor
Units: 3-0-6
The health care industry as context for medical economic
studies and examinations of the determinants of health outcomes. Focus on
specific principles and tools of economics and their applicability to the
treatment of illnesses such as hypertension, depression, anxiety, anemia, and
gastrointestinal disease. Perspectives of employer, health provider,
pharmaceutical firms, and government regulators in the US and abroad.
S. N. Finkelstein, E. R. Berndt
Prereq.: Permission of instructor
Units: 2-0-4
URL: http://web.mit.edu/15.561/www/
Subject covers technology concepts and trends underlying
current and future developments in information technology, and fundamental principles
for the effective use of computer-based information systems. Special emphasis
on networks and distributed computing, including the World Wide Web. Other
topics include: hardware and operating systems, software development tools and
processes, relational databases, security and cryptography, enterprise
applications and business process redesign, and electronic commerce. Hands-on
exposure to Web, database, and graphical user interface (GUI) tools. Primarily
for Sloan master's students.
C. N. Dellarocas, B. N. Grosof, T. W. Malone
Prereq.: Permission of instructor
Units: 4-0-8
You must
pre-register and participate in Sloan's Prioritization process to take this
subject.
Lecture: TR1-2:30
(E51-145) Recitation: F11 (E56-270) +final
Broad coverage of technology concepts underlying modern
computing and information management. Topics include computer architecture and
operating systems, relational database systems, graphical user interfaces,
networks, client/server systems, enterprise applications, cryptography, and the
World Wide Web. Hands-on exposure to Internet services, Microsoft Access
database management system, and Lotus Notes.
C. Dellarocas
Prereq.: Permission of instructor
Units: 3-0-6
You must pre-register and participate in Sloan's Prioritization proc