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 process to take this
subject.
Lecture: MW1-2:30
(E51-345)
Concepts, frameworks, tools, techniques, and processes that
assist management in its interaction with and direction of computer-based
information systems today. Discusses the impact of the Internet, changes in the
IT industry, and changes in other industries as a result of IT. Also notes the
redesign of information flows to meet the needs of both control and empowerment
in the era of the global information infrastructure and networked
organizations. Emphasizes managerial point of view and organizational issues
involved in managing a firm's information resources.
W. Orlikowski
Prereq.: Permission of instructor
Units: 3-0-6
Presents a theoretical background for important topics in
information technology such as efficiency of algorithms, computer performance
evaluation, computer communications, parallel processing, and database
management systems. Important current topics, such as integrating information
from heterogeneous sources, are stressed. Theoretical foundations are drawn
from areas such as combinatorics, queuing theory, concurrency control, theorem
proving, and artificial intelligence. Primarily for doctoral students and
advanced master's students. Offered every third year.
Consult S. E. Madnick.
Prereq.: Permission of instructor
Units: 3-0-6
Credit cannot also be received for 15.565J or ESD.565J
Explores critical issues of communications and connectivity
among global and Internet-based information systems from strategic,
organizational, and technical perspectives. Strategic connectivity:
globalization and integration of information, competitive forces, interlinked
value chains. Physical connectivity: protocols and technologies of local-area
and wide-area, and Internet communications networks. Logical connectivity: distributed
databases, data extraction from Web sites, semantic reconciliation among
heterogeneous sources. Organizational connectivity: loosely coupled
organizations, development of standards, motivating strategic alliances.
S. E. Madnick
Prereq.: 15.810 or equivalent, 15.061 or equivalent
Units: 3-0-6
URL: http://web.mit.edu/15.825/www/15825.html
Modern databases and computer models for supporting tactical
and strategic decisions in marketing. Special attention to the growing role of
very large databases collected at the point of sale and over the internet.
Basic modeling approaches. Multinomial logit and discrete choice models. Data
mining. Models for specific decision areas, including price, promotion,
advertising, distribution, and sales force. Integrative models for the
marketing mix.
J. D. C. Little
Prereq.: 15.810 or
permission of instructor
Units: 3-0-6
You must
pre-register and participate in Sloan's Prioritization process to take this
subject.
Lecture: MW10-11:30
(E56-270)
Examines the special challenges of marketing high-tech
products and focuses on dynamic product contexts fraught with significant
technological and market uncertainty. Most reading materials are drawn from the
Information Technology industry: computer hardware and software, consumer
electronics, telecommunications, and content. Students encouraged to interject
parallels from non-IT, but otherwise high-tech settings during class
discussion. Samples both consumer and business-to-business (industrial)
marketing contexts.
E. Dahan
Stuart M. Shieber
Half course (fall term). M., W., F., at 10. EXAM GROUP: 3
Introduction to the intellectual enterprises of computer
science. Algorithms: their design, specification, and analysis. Software
development: problem decomposition, abstraction, data structures,
implementation, debugging, testing. Architecture of computers: low-level data
representation and instruction processing. Computer systems: programming
languages, compilers, operating systems. Computers in the real world: networks,
security and cryptography, artifical intelligence, social issues. Laboratory
exercises include extensive programming in the C language and experimenting
with and analyzing software systems.
Note: No previous computer experience required.
Henry H. Leitner
Half course (spring term). Tu., Th., 1–2:30. EXAM GROUP: 15,
16
Abstract models for computational processes and their
concrete realizations. Functional, imperative, and object-oriented styles of
programming; processor and memory architectures; interpretation and compilation
of programming languages. State-space search, finite-state processes, formal
logic, data and functional abstraction, and syntactic and semantic formalisms
as examples of useful abstractions. The engineering of complex software. Laboratory
exercises using LISP, C++, and Java.
Prerequisite: Computer Science 50 or equivalent.
Harry R. Lewis
Half course (fall term). Tu., Th., 10–11:30. EXAM GROUP: 13
General introduction to formal systems and the theory of
computation. Elementary treatment of automata, formal languages, computability,
uncomputability, computational complexity, NP–completeness, and mathematical
logic.
Michael D. Mitzenmacher
Half course (spring term). M., W., 1–2:30. EXAM GROUP: 6, 7
Design and analysis of efficient algorithms. Data structure
representations and their use for provably efficient implementation of abstract
operations: searching, sorting, set manipulation. Memory management. Graph
algorithms. General algorithm design techniques.
Prerequisite: Computer Science 51; some exposure to discrete
applied mathematics, such as Applied Mathematics 106 or 107 or Computer Science
121 or Statistics 110, is helpful.
Norman Ramsey
Half course (fall term). M., W., F., at 11. EXAM GROUP: 4
Intellectual tools needed to design, evaluate, and choose
programming languages. Historical influence of theory, software engineering,
and implementation technique on language design. Case studies, reinforced by
programming exercises. Emphasizes advanced languages, abstraction mechanisms.
Includes functional, object-oriented, and logic paradigms. Focuses on ideas and
techniques most relevant to practitioners, but covers theoretical topics
crucial for intellectual rigor: specification based on abstract syntax, lambda
calculus, type systems,and dynamic semantics. Grounding sufficient to read
professional literature.
Prerequisite: Computer Science 121. Students must have
excellent programming skills. Must be comfortable with recursion and with basic
mathematical ideas and notations.
Half course (fall term). Hours to be arranged.
Design principles for modern distributed database systems.
Topics include: extended E/R, relational and object-oriented data models; query
processing, persistence, concurrency control, back-up and recovery; database
connectivity; Java and XML languages; Web information organization, indexing
and retrieval; search engines architecture and algorithms.
Note: Expected to be given in 2001–02.
Prerequisite: Computer Science 161 or permission of
instructor.
Avrom J. Pfeffer
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
Introduction to artificial intelligence, focusing on
problems of perception, machine learning and reasoning under uncertainty.
Supervised learning algorithms. Neural networks and applications to character
recognition. Statistical pattern recognition. Bayesian networks:
representation, inference and learning. Hidden Markov models and applications
to speech recognition. Markov decision processes and reinforcement learning.
Prerequisite: Computer Science 51, Computer Science 121 and
Statistics 110, or equivalent.
Barbara J. Grosz
Half course (fall term). M., W., 1–2:30. EXAM GROUP: 6, 7
Introduction to AI focused on approaches to problems of
reasoning about action. Search and game-playing. Knowledge representation.
Partial-order planning: representations of actions; techniques for handling
goal interactions. Resource-limited planning; situated agents. Discussion of
relevant work in philosophy and decision theory; applications to vision,
language, robotics.
Prerequisite: Computer Science 51; Computer Science 121 (may
be taken concurrently).
Michael O. Rabin
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13,
14
Topics in modern cryptography. Primality testing, finite
fields, elliptic curves. Protocols: Public-key encryptions, digital signatures,
key exchanges, zero-knowledge proofs, authentication oblivious transfer, secret
sharing, proactive security, fair contract signing, distributed agreements.
Foundations: Probablistic encryption and semantic security. Attacks and
countermeasures: Non-malleabilty, plaintext awareness and proofs of plaintext
knowledge. Absolutely secure encryptions. Prerequisites will be discussed in
sections.
Michael D. Mitzenmacher
Half course (fall term). Tu., Th., 2:30–4. EXAM GROUP: 16,
17
The course will focus on how Markov chains and random
processes are used to analyze algorithms and network behavior. Reading current
research in the area will be required. Topics may include heavy-tailed
distributions, load balancing, stochastic bin-packing, and models of the Web.
Prerequisite: Computer Science 124. Preferably additional
probability, such as in Computer Science 224r, Computer Science 226r,
Statistics 110, or Mathematics 191.
Michael O. Rabin
Half course (fall term). Hours to be arranged.
Exploration of the surprising efficacy of randomization in
the solution of algorithmic and general computer science problems. Applications
include number theoretic algorithms, cryptographic protocols, computations in
finite fields, computational geometry. CS applications will include routing in
networks, parallel algorithms, pattern matching, agreement protocols for
distributed systems. We shall also deal with programs that check and correct
their own work and with Probabilistically Checkable Proofs (PCP). The
probability theory prerequisites will be covered.
Note: Expected to be given in 2001–02.
Michael O. Rabin
Half course (fall term). Hours to be arranged.
A survey of important computer algorithms for numerical and
data manipulation problems and their applications in actual computing
situations. Topics include combinatorial algorithms, string matching, FFT and
its applications, algebraic computations, randomized algorithms in algebra
number theory and geometry, maximal flows, error correcting codes, public key
cryptography, protocols for distributed systems, and parallel algorithms.
Note: Expected to be given in 2002–03.
Leslie G. Valiant
Half course (spring term). Tu., Th., 2:30–4. EXAM GROUP: 16,
17
Possibilities of and limitations to performing learning by
computational agents. Topics include computational models, polynomial time learnability,
learning from examples and learning from queries to oracles. Computational
limitations. Statistical limitations. Applications to Boolean functions,
automata and geometric functions. Learning algorithms for models of neural
computation.
Prerequisite: Computer Science 121 or equivalent.
Stuart M. Shieber
Half course (spring term). Hours to be arranged.
Seminar providing background and current research in
specific topics drawn from one or more of computer-human interfaces,
information, retrieval, and information visualization. Intensive lab component
emphasizes small group design and implementation of systems in these areas.
Note: Expected to be given in 2001–02.
Prerequisite: Computer Science 51 and experience developing
large software systems as evidenced by successful completion of a systems
course requiring a large project.
Avrom J. Pfeffer
Half course (fall term). Hours to be arranged.
In-depth introduction to formalisms for knowledge
representation and techniques for reasoning and planning. Topics: formal
logic-based representations; probabilistic reasoning; nonmonotonic logics;
truth-maintenance systems; qualitative reasoning; inheritance hierarchies;
computational approaches to reasoning about actions and time, including actions
of multiple agents, nonlinear planning, plan recognition; reasoning about
knowledge, belief, and action.
Note: Expected to be given in 2001–02.
Prerequisite: Computer Science 182, or permission of
instructor.
Avrom J. Pfeffer
Half course (fall term). M., W., 2:30–4. EXAM GROUP: 7, 8
In-depth study of principles and techniques for
probabilistic reasoning and decision-theoretic planning. Topics include:
Bayesian networks and Markov networks; exact and approximate probabilistic
inference algorithms; learning Bayesian networks from data; temporal
probability models; integrating logic and probability; influence diagrams;
Markov decision processes; reinforcement learning.
Prerequisite: Computer Science 181 or permission of
instructor.
Barbara J. Grosz
Half course (spring term). Tu., Th., 11:30–1. EXAM GROUP:
13, 14
Theories and techniques for multi-agent planning, including
formal models of rational agents, collaborative plans, and social systems;
computational approaches to distributed planning and problem solving,
negotiation, and decision theory for planning; collaborative systems design.
Prerequisite: Computer Science 182 or permission of
instructor.
Stuart M. Shieber
Half course (spring term). Tu., Th., 2:30–4. EXAM GROUP: 16,
17
Principles and techniques of natural language processing,
including grammar formalisms, syntactic analysis, semantic interpretation, and
associated algorithms.
Prerequisite: Computer Science 121 and 152.
Frederick P. Roth (Medical School) 3912
Cell Biology
with the Medical School as GN 714.0.
Robert E. Kingston (Medical School) and Fred Winston
(Medical School)
Half course (spring term). Tu., 1–4.
This course will cover both biochemical and genetic studies
in regulatory mechanisms. We will discuss a small number of topics in depth,
using the primary literature as the main source of information. Each area of
research covered will be analyzed in terms of the conceptual basis for its
study, its advancement and evolution, and the experimental approaches that were
used. Topics will range from prokaryotic transcription to eukaryotic
development.
Note: Offered jointly with the Medical School as GN 703.0.
Prerequisite: BCMP 200 and Genetics 201.
Nadia Rosenthal (Medical School) and David Hausman (Medical
School) and associates
Half course (fall term). M., F., 9:30–12.
The focus of this course is on the scientific, clinical, and
ethical aspects of modern human genetics. Basic science lectures covering
genetic approaches and molecular underpinnings of inherited diseases are
integrated with patient presentations and discussion. An outside project puts
each student in direct contact with clinicians, researchers, and patients
dealing in a particular disorder. During the first portion of the semester
fundamental principles of human genetics are presented to the class. During
these early sessions, students with stronger backgrounds meet in alternative
sections with leading researchers to discuss related advanced topics based on
reading of primary literature.
Note: Offered jointly with the Medical School as HT 160.
Irvin C. Schick
Half course (spring term). M., W., 4:30–6. EXAM GROUP: 9
Introduction to analytical and numerical methods for
optimization of deterministic and stochastic systems; survey of linear and
nonlinear programming, game theory, decision analysis, Markov chains, queuing
theory and simulation. Examples taken from a variety of fields. A conceptual
introduction to materials covered in depth in Engineering Sciences 201, 202,
205, and 210. Segments of the weekly problem sets can be done on PCs, if
desired.
Note: Students who have no background in probability should
be prepared to do some extra work. Some PC experience useful but not necessary.
Prerequisite: Applied Mathematics 21b or Mathematics 21b and
some knowledge of probability and statistics at the level of Statistics 110 or
Engineering Sciences 101.
David A. Edwards
Half course (spring term). M., W., F., at 11. EXAM GROUP: 4
Introduces students to discovery and pre-clinical and
clinical development in the genomics, drug delivery, and medical device
industries. Overviews biological systems including the immune and circulatory
systems, and the lungs, heart and brain. Describes classes of drugs including
small molecules and proteins, and the chemistry and engineering involved in
drug delivery systems such as polymeric microspheres, gene vectors, pulmonary
inhalers, and transdermal patches. Lectures or additional meetings will include
speakers from the biotech community (senior officers of biotech companies and
leading scientists). Students participate in the class through group projects
in which they will research industries, technologies, preclinical and clinical
developments, and markets.
Note: Expected to be omitted in 2001–02.
Prerequisite: An understanding of organic chemistry is
strongly recommended. Exceptions will be made with approval of the instructor.
Roger W. Brockett
Half course (spring term). M., W., F., at 10. EXAM GROUP: 3
Mathematical analysis of decision making under uncertainty.
Axiomatic derivation of subjective probability and utility. Decision trees,
normal and extensive form, value of information. Bayesian inference. Comparison
with classical forms of inference. Optimal sample size. Estimation and sequential
decision problems. Normal and regression models. Applications to business
decisions, engineering problems, sampling, etc.
Prerequisite: Applied Mathematics 21a,b or Mathematics
21a,b, and Statistics 110 or equivalents.
Steve C. Wang
Half course (fall term). Tu., Th., 10–11:30, and a section
meeting to be arranged. EXAM GROUP: 12, 13
An introduction to data analysis using multiple regression.
Topics may include model building and diagnostics, graphical checks of
assumptions, transformations, multivariate graphics and visualization,
exploratory data analysis, tests of significance and confidence intervals, and
logistic regression. The course will emphasize analysis and investigation of
real datasets using computer software.
Prerequisite: Statistics 100 or equivalent.
John Barnard
Half course (spring term). Tu., Th., 10–11:30. EXAM GROUP:
12, 13
An introduction to the application and theory of generalized
linear models. Emphasis is on understanding models and applying them to data.
Topics include likelihood theory, exponential families, model specification,
model checking and diagnostics, logistic and ordinal regression, log-linear
models, quasi-likelihood, generalized estimating equations, and generalized
linear mixed models. Applications are drawn from a variety of fields, including
medicine, biology, and the social sciences.
Prerequisite: Statistics 111 or equivalent and Statistics
139 or equivalent.
Alan Zaslavsky (Medical School)
Half course (fall term). M., W., 4–5:30. EXAM GROUP: 8, 9
Methods for design and analysis of sample surveys.
Techniques for sample design, with examples from some widely used current
surveys. Estimation methods (including calculation and use of sampling weights)
and variance estimation methods (including resampling methods). Several guest
lectures on nonstatistical aspects of survey methodology such as questionnaire
design and validation. Other topics include variance estimation for complex
surveys and estimators, nonresponse, missing data, and small-area estimation.
Prerequisite: Statistics 111 or 139, or permission of
instructor.
Arthur P. Dempster
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13,
14
A survey of models and analysis methods giving roughly equal
time to temporal domain, frequency domain, and nonlinear (including chaotic)
systems. Coverage will be broad rather than deep, and will include current
developments such as hidden Markov models, multipaper methods of spectral
estimation, and methods for assessing Lyapunov coefficients.
Prerequisite: Statistics 111 or equivalent.
David van Dyk
Half course (spring term). Hours to be arranged. EXAM GROUP:
6
The development of a Bayesian approach to the related
problems of decision and forecasting. Decision topics will include utility,
loss, decision rules, risk, admissibility of decision rules, and decision
theoretic aspects of sequential analysis. Forecasting will be developed through
the dynamic linear model and include topics such as sequential analysis and
smoothing; models for polynomial trends, seasonal trends, and adjustment for
covariates; and forecast intervention, monitoring, and error analysis. Theory
and computational methods will be developed with a strong emphasis on
applications to a variety of data sets.
Note: Expected to be given in 2001–02.
Prerequisite: Statistics 110 or 139 or equivalent.
Carl N. Morris
Half course (fall term). Tu., Th., 1–2:30. EXAM GROUP: 15,
16
Random variables, their distributions and densities.
Families and exponential families of distributions. Expectation. Independence,
product spaces, and joint distributions. Types of convergence. Limit theorems
(weak and strong laws, central limit problem). Conditional probability and
expectation, multivariate Normal distribution, particular examples of conjugate,
marginal, and conditional distributions. Inequalities, approximations, and
stochastic simulation. Sampling distributions, likelihood function,
sufficiency, and information.
Prerequisite: A course in probability and statistics at
least at the level of Statistics 110, 111.
Carl N. Morris
Half course (spring term). Tu., Th., 11:30–1. EXAM GROUP:
13, 14
Introduction to statistical inference. Frequency, Bayesian,
and decision-theoretic approaches. Likelihood, sufficiency, multivariate Normal
distribution, and exponential families. Testing hypotheses and estimation.
Maximum likelihood estimation, likelihood ratio tests, linear models, models
for frequency data, large and moderate sample approximations, including the
delta method.
Prerequisite: Advanced calculus, Statistics 210, or
equivalent.
David van Dyk
Half course (fall term). M., W., F., at 10. EXAM GROUP: 3
Begins with basic Bayesian models, whose answers often
appear similar to classical answers, followed by more complicated hierarchical
and mixture models with nonstandard solutions. Includes methods for monitoring
adequacy of models and examining sensitivity of conclusions to change in
models. Throughout, emphasis on drawing inferences via computer simulation
rather than mathematical analysis.
Prerequisite: Statistics 110 and 111.
David van Dyk
Half course (spring term). Th., 1–2:30, M., 1–3:30. EXAM
GROUP: 6, 7, 8, 15, 16
A study of computing methods commonly used in statistics.
Topics include generation of random numbers, Monte Carlo methods, optimization
methods, numerical integration, and advanced Bayesian computational tools such
as the Gibbs sampler, Metropolis Hastings, the method of auxiliary variables,
marginal and conditional data augmentation, slice sampling, exact sampling, and
reversible jump MCMC. Computer programming exercises apply the methods
discussed in class.
Prerequisite: Linear algebra, Statistics 111, and knowledge
of acomputer programming language. Statistics 220 is recommended.
Half course (spring term). Hours to be arranged.
A survey of multivariate analysis. Normal distribution
theory, estimation, and hypothesis testing. Multivariate techniques, including
cluster analysis, multidimensional scaling, principal component analysis,
discriminant analysis, and multiple regression. These techniques are applied to
data sets.
Note: Expected to be given in 2001–02.
John Barnard
Half course (fall term). M., F., 2–3:30. EXAM GROUP: 7, 8
Methods for handling incomplete data sets with general
patterns of missing data, emphasizing likelihood-based and Bayesian approaches.
Focus is on the application and theory of iterative maximization methods,
iterative simulation methods, and multiple imputation. Includes coverage of
some multivariate tools and theory relevant to missing data problems. Real
examples are drawn from a variety of fields, including health sciences, history
of science, and government.
Note: Expected to be given in 2001–02.
Prerequisite: A course in probability (Statistics
110-level), a course in theoretical statistics (Statistics 111-level), and
knowledge of regression and linear algebra (Statistics 139-level).
John Barnard
Half course (fall term). Tu., Th., 10–11:30, M., 7–9 p.m.
EXAM GROUP: 1, 12, 13
Besides the applications done jointly with Statistics 139,
students meet separately to develop the theory (multivariate normal, maximum
likelihood, likelihood ratio tests, Gauss-Markov, etc.) of linear models.
Students do some of the homework assignments from Statistics 139, but also
other assignments that differ and are more advanced. Grading is separate from
Statistics 139.
Prerequisite: Probability and statistics at the level of
Statistics 110 and 111.
Jun S. Liu 3760 and Wesley Philip Wong
Half course (fall term). Hours to be arranged.
Jun S. Liu 3760
Half course (spring term). Hours to be arranged.
Note: Will meet at the School of Public Health.
Efthimios Kaxiras
Half course (fall term). M., W., F., at 11. EXAM GROUP: 4
Functions of a complex variable: mapping, integration,
branch cuts, series. Fourier series; Fourier and Laplace transforms; transforms
applied to differential equations and data analysis; convolution and
correlation; elementary probability theory.
Note: Applied Mathematics 105a and 105b are independent
courses, and may be taken in any order.
Prerequisite: Applied Mathematics 21a and 21b, or
Mathematics 21a and 21b.
Leslie G. Valiant
Half course (spring term). Tu., Th., 10–11:30. EXAM GROUP:
12, 13
Topics in combinatorial mathematics that find frequent
application in computer science, engineering, and general applied mathematics.
Specific topics taken from graph theory, enumeration techniques, optimization
theory, combinatorial algorithms, and discrete probability.
Donald G. M. Anderson
Half course (fall term). Tu., Th., 10–11:30. EXAM GROUP: 12,
13
Elementary numerical methods and their computer
implementation: linear and nonlinear equations; interpolation, differentiation
and quadrature; ordinary differential equation initial and boundary value
problems.
Note: Expected to be omitted in 2001–02. Offered in
alternate years.
Prerequisite: Applied Mathematics 21a and 21b, or
Mathematics 21a and 21b; Computer Science 50, or equivalent.
Donald G. M. Anderson
Half course (fall term). Tu., Th., 2:30–4. EXAM GROUP: 16,
17
An algorithmic approach to topics in matrix theory which
arise frequently in applied mathematics: linear equations, pseudoinverses,
quadratic forms, eigenvalues and singular values, linear inequalities and
optimization, linear differential and difference equations.
Note: Expected to be given in 2001–02. Offered in alternate
years.
Prerequisite: Applied Mathematics 21b, or Mathematics 21b,
or equivalent.
Half course (fall term). Hours to be arranged.
A study of behaviors that characterize nonlinear ordinary
differential equations: self-sustained oscillations, strange attractors, chaos.
System response to pulsatile and periodic stimuli; iterated mapping and period
doubling. Averaging methods. Mutual entrainment of oscillators. Applications
are made to electrical, mechanical, and chemical systems and to biological
rhythms.
Note: Expected to be given in 2001–02.
Prerequisite: Calculus to the level of Applied Mathematics
21b or Mathematics 21b.
William H. Bossert
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13,
14
Computational methods at a sophisticated analytic level.
Practical exercises emphasized. Linear algebra; polynomial and rational
function extrapolation; Chebyshev methods; special functions; nonlinear root
finding; one- and multidimensional extremization; eigensystems; Fourier
transform methods; linear and nonlinear model fitting; adaptive methods for
differential equations; stochastic methods for integration and optimization of
multidimensional functions.
Prerequisite: Mathematics at the level of Applied
Mathematics 105b. A previous course in computing is not required but ability to
program in Fortran or C will be useful.
Dr. G. Colditz
2.5 credits
Seminars. One 3-hour session each week.
Concerned with the use of existing data to inform clinical
decision making and health care policy, the course focuses on research
synthesis (meta-analysis). The principles of meta-analytic statistical methods
are reviewed, and the application of these to data sets is explored.
Application of methods includes considerations for clinical trials and observational
studies. The use of meta-analysis to explore data and identify sources of
variation among studies is emphasized, as is the use of meta-analysis to
identify future research questions.
Course Activities: Students prepare a protocol to conduct a
meta-analysis and use existing meta-analysis software to apply principles
outlined in the course to data sets provided for this purpose.
Dr. E. J. Orav
2.5 credits
Lectures. Five 2-hour sessions each week.
Presents additional biostatistical techniques that commonly
appear in the analysis of clinical databases and trials. This course will move
at a faster pace than the alternative BIO 206t while covering all of the same
topics (contingency tables, log-rank tests, paired and matched analyses,
analysis of variance and multiple comparisons procedures). In addition, linear
and logistic regression will be introduced.
Course Note: BIO 206s required; no auditors.
Dr. R. Glynn
5 credits
Lectures, laboratories. Two 1.5-hour sessions each week. One
1.5-hour lab each week.
Emphasizes concepts and methods for analysis of data which
are categorical, rate-of-occurrence (e.g., incidence rate), and time-to-event
(survival duration). Stresses applications in epidemiology, clinical trials,
and other public health research. Topics include measures of association, 2x2
tables, stratification, matched pairs, logistic regression, model building,
analysis of rates, and survival data analysis using proportional hazards
models.
Course Note: BIO 200ab, BIO 201ab or BIO 200s and BIO 200t
or signature of instructor required; lab or section times to be announced at
first meeting.
Dr. J. Ibrahim
5 credits
Lectures, laboratories. Two 1.5-hour sessions each week; one
1-hour lab each week.
Covers analysis of variance and regression, including
details of data-analytic techniques and implications for study design. Also
included are probability models and computing. Students learn to formulate a
scientific question in terms of a statistical model, leading to objective and
quantitative answers.
Course Note: BIO 200ab, BIO 201ab, or signature of
instructor required; lab or section time will be announced at first meeting.
Dr. E. J. Orav
5 credits
Lectures. Two 1.5-hour sessions each week. One 1.5-hour lab
each week.
This course will introduce students involved with clinical
research to the practical application of multiple regression analysis. Linear
regression, logistic regression and proportional hazards survival models will
be covered, as well as general concepts in model selection, goodness-of-fit,
and testing procedures. Each lecture will be accompanied by a data analysis
using SAS and a classroom discussion of the results. The course will introduce,
but will not attempt to develop the underlying likelihood theory.
Course Note: Previous introductory level statistics course and
SAS programming ability required; lab or section time will be announced at
first meeting.
Dr. K. Stanley, Dr. R. Gelber
2.5 credits
Lectures. Five 2-hour sessions each week.
Designed for individuals interested in the scientific,
policy, and management aspects of clinical trials. Topics include types of
clinical research, study design, treatment allocation, randomization and
stratification, quality control, sample size requirements, patient consent, and
interpretation of results. Students design a clinical investigation in their
own field of interest, write a protocol for it, and critique recently published
medical literature.
Course Note: BIO 200ab, BIO 201ab, BIO 206s, BIO 207t, or
BIO 200s and BIO 200t or signature of instructor required.
Dr. R. Xu
5 credits
Lectures. Two 2-hour sessions each week. One 1-hour optional
lab each week.
This course will cover topics in both discrete data analysis
(25% of class) and applied survival analysis (75% of class). The course will
begin with a review of sampling plans and contingency table for discrete data.
Further topics in discrete data analysis will include logistic regression,
exact inference, and conditional logistic regression. This short survey of
discrete data topics will provide a natural transition to analysis of survival
data. Survival topics include: hazard, survivor, and cumulative hazard
functions, Kaplan-Meier and actuarial estimation of the survival distribution,
comparison of survival using log rank and other tests, regression models
including the Cox proportional hazards model and accelerated failure time
model, adjustment for time-varying covariates, and use of parametric
distributions (exponential, Weibull) in survival analysis. Class material will
include presentation of statistical methods for estimation and testing, along
with current software (SAS, Stata, Splus) for implementing analyses of discrete
data and survival data. Applications to real data will be emphasized.
Course Note: BIO 210cd, BIO 213ab, or BIO230ab required, or
permission of instructor.
Dr. R. Davis
2.5 credits
Lectures. Five 2-hour sessions each week.
This course will cover the common approaches to the display
and analysis of survival data, including Kaplan-Meier curves, log rank tests,
and Cox proportional hazards regression. Computing, using SAS, will be an
integral component of the course.
Course Note: BIO 210cd, BIO 211cd, BIO 213ab or signature of
instructor required.
Dr. B. Coull
5 credits
Lectures, laboratories. Two 2-hour sessions each week.
This course covers modern methods for the analysis of
repeated measures, correlated outcomes and longitudinal data, including the
unbalanced and incomplete data sets characteristic of biomedical research.
Topics include an introduction to the analysis of correlated data, repeated
measures ANOVA, random effects and growth curve models, and generalized linear
models for correlated data, including generalized estimating equations (GEE).
Course Activities: Homework assignments will focus on data
analysis in SAS using PROC GLM, PROC MIXED, and PROC GENMOD.
Course Note: BIO 211cd, BIO 213ab, or BIO 232ab, or
signature of instructor required; lab or section time will be announced at
first meeting.
Dr. J. Rogus, Dr. A. Doria
2.5 credits
Lectures, laboratories. Two 2-hour sessions each week.
This course will introduce students to the basic concepts of
genetics and molecular biology that are necessary for an understanding of the
genetic basis of disease. The course material consists of two main topics,
molecular biology and genetic epidemiology, plus case studies. Specific areas
to be covered include 1) the structure and characteristics of the human genome,
the Human Genome Project, and laboratory methods in molecular biology, and 2)
heritability, gene mapping, simple and complex genetic traits, linkage, and
linkage disequilibrium.
Dr. L. Palmer (P), Dr. N. Laird (S)
2.5 credits
To be given 2001-2002; offered alternate years.
Lectures: Two 2-hour sessions each week.
This is an introductory course covering statistical methods
for the
analysis of family data, with emphasis on gene mapping.
Topics covered
will include: allele frequency estimation, classical
segregation and
linkage analysis, multipoint linkage tests, model-free
linkage analysis, general pedigree analysis, family-based association analysis
and study
design for complex genetic traits. Students will gain
exposure to some
of the methods and computer tools available for gene mapping
and genetic analysis, and begin to read and evaluate statistical human genetics
literature.
Course Note: BIO227a or signature of instructor required.
Dr. M. Bonetti
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One
2-hour lab each week.
A first course in probability fundamental to the
biostatistics program. Topics include axiomatic foundations, frequency and
personal concepts of probability, combinatorics, discrete and continuous sample
spaces, independence and conditional probability, random variables, expectation
operator, moments, generating functions and characteristic functions, standard
distributions, transformations, sampling distributions related to the normal
distribution, convergence concepts, weak and strong laws of large numbers, the
central limit theorem, and elements of stochastic processes.
Course Note: Multi-variable calculus (one or two semesters
beyond elementary calculus) suggested; signature of instructor required; lab or
section time to be announced at first meeting.
Dr. M. Zelen
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One
1.5-hour lab each week.
A fundamental course in statistical inference. Discusses
general principles of data reduction: exponential families, sufficiency,
ancillarity and completeness. Describes general methods of point and interval
parameter estimation and the small and large sample properties of estimators:
method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and
Lehmann-Scheffe theorems, information inequality, asymptotic relative
efficiency of estimators. Describes general methods of hypothesis testing and
optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests,
score and Wald tests, uniformly and locally most powerful tests, asymptotic
relative efficiency of tests.
Course Note: BIO 230ab or signature of instructor required;
lab or section time to be announced at first meeting.
Dr. D. Wypij
5 credits
Lectures, laboratories (optional). Two 2-hour sessions each
week. One 1.5-hour lab each week.
This course focuses on the analysis of categorical data and
count data, and provides an introduction to methods for analysis of survival
data. Topics include a review of sampling plans, analysis of contingency
tables, large sample and exact methods for constructing confidence intervals
and hypothesis tests, measures of association, logistic regression, and
log-linear analysis. Survival topics will include estimation of survival
distributions, comparison of groups, and regression models such as the Cox
proportional hazards model and the accelerated failure time models.
Course Note: BIO 210cd and BIO 222ab or BIO 232ab, or
signature of instructor required. Lab or section time to be announced at first
meeting.
Dr. F. Vaida
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One
2-hour lab each week.
This is an advanced course in data analysis for linear
models - regression and analysis of variance. Estimation methods (maximum
likelihood and least squares) and issues of inference (confidence intervals,
hypothesis testing, analysis of residuals) are presented from a theoretical and
data analysis perspective.
Course Note: BIO 232ab and BIO 231cd, or signature of
instructor required; familiarity with matrix algebra and BIO 211cd or
equivalent recommended. Lab or section time to be announced at first meeting.
Dr. M. Hughes
2.5 credits
To be offered 2001-2002; offered alternate years.
Lectures. Two 2-hour sessions each week.
Presents the theory and application of nonparametric
methods. Topics include permutation tests, permutation limit theorems, 2-sample
rank tests and their asymptotic efficiency, k-sample rank tests, 1-sample tests
of location, paired comparsions, rank tests for symmetry and independence, and
analogues of linear modeling based on ranks.
Course Note: BIO231cd required.
Dr. L.J. Wei
5 credits
Lectures. Two 2-hour sessions each week.
Discusses the theoretical basis of concepts and
methodologies associated with survival data and censoring, nonparametric tests,
and competing risk models. Much of the theory is developed using counting
processes and martingale methods. Material is drawn from recent literature.
Course Note: BIO 231cd and BIO 233cd required.
Dr. N. Laird
5 credits
Lectures. Two 2-hour sessions each week.
Presents classical and modern approaches to the analysis of
multivariate observations, repeated measures, and longitudinal data. Topics
include the multivariate normal distribution, Hotelling's T2, MANOVA, the
multivariate linear model, random effects and growth curve models, generalized
estimating equations, statistical analysis of multivariate categorical
outcomes, and estimation with missing data. Discusses computational issues for
both traditional and new methodologies.
Course Note: BIO 231cd and BIO 235ab required.
Dr. V. De Gruttola
5 credits
Not to be given 2001-2002; offered alternate years.
Lectures. Two 2-hour sessions and one 2-hour lab each week.
Discusses those aspects of statistical theory and practice
relevant to the design of scientific investigations in the health sciences.
Topics include sample size considerations, basic principles of experimental
design (randomization, replication, and balance), block designs, factorial
experiments, response surface modeling, clinical trials, adaptive designs,
cohort studies, early detection trials, and double sampling techniques.
Course Note: BIO 235ab or signature of instructor required;
minimum enrollment of 10 students required.
Dr. R. Gray
5 credits
Lectures. Two 2-hour sessions each week.
A course in computing algorithms useful in statistical
research and advanced statistical applications. Topics include computer
arithmetic, matrix algebra, numerical optimization methods with application to
maximum likelihood estimation and GEEs, spline smoothing and penalized
likelihood, numerical integration, random number generation and simulation
methods, Gibbs sampling, bootstrap methods, missing data problems and EM,
imputation, data augmentation algorithms, and Fourier transforms.
Course Note: BIO 235ab or consent of instructor and
proficiency with C or Fortran programming required.
Dr. S. Normand
5 credits
Lectures. Two 2-hour sessions each week.
This course examines basic aspects of the Bayesian paradigm
including Bayes’ theorem, the likelihood principle, prior distributions,
posterior distributions, and predictive distributions. General topics include
Bayesian analysis of linear models, generalized linear models, survival models,
and random effects models. Computations using Markov chain Monte Carlo methods
are discussed. Bayesian methods in meta-analysis and the design and analysis of
clinical trials will be examined.
Course Note: BIO 230ab, BIO 231cd and BIO 232ab or signature
of instructor required.
Dr. C. Mehta
2.5 credits
To be given 2001-2002; offered alternate years.
Lectures. Two 2-hour sessions each week.
This course deals with exact nonparametric methods of
inference. These methods use fast numerical algorithms to permute the observed
data in all possible ways, and thereby derive exact distributions for the test
statistics of interest without making any distributional or large-sample
assumptions. In contrast, standard parametric methods of inference make
distributional assumptions about the data, while standard nonparametric methods
of inference rely on asymptotic theory to derive approximate distributions for
the test statistics. Exact nonparametric methods are particularly important for
small, sparse or unbalanced data where the usual asymptotic theory breaks down.
This course will cover exact inference for one, two and K-sample problems,
ordered and unordered RxC contingency tables, 2x2 and 2xC contingency tables
with or without stratification, and logistic regression. A unified view,
encompassing both continuous and categorical data, will be presented based on
the permutation principle. Modern algorithmic advances that make exact
permutational inference computationally feasible will be treated in depth. The
methods will be illustrated by several biomedical data sets. This course will
use StatXact and LogXact statistical packages.
Dr. K. Lunetta
2.5 credits
Not to be given 2001-2002; offered alternate years.
Lectures. One 2-hour session each week.
This course will introduce students to statistical
procedures for investigating inheritance in humans. Methods for human gene
mapping, such as family-based tests of association and linkage, will be
emphasized. Readings from selected texts and current literature. A brief
introduction to human genetics will be provided.
Course Note: BIO 230ab and BIO 231cd required.
Dr. R. Betensky
2.5 credits
Lectures. One 2-hour sessions each week.
The aim of this course is to strengthen students' background
in analysis and operational use of mathematics. The course will emphasize the
application of several fundamental results, and not the proofs of these
results. Students will work several problems which illustrate fundamental
mathematical operations. Topics include concepts of convergence (e.g., power
series, Taylor's series), functions (limits, continuity, step functions,
L'Hopital's rule, differentiability), integration (Riemann, Stieltjes,
Lebesque), operations convergence theorem, complex variables (e.g., Laplace
transforms, Fourier transforms, inversion formulas).
Course Note: BIO 230ab required; no auditors.
Dr. W. Wong
5.0 credits
Lectures. One 4-hour session each week.
With the rapid advances in molecular biology over the past
decade, the need for quantitative methods to analyze the vast amounts of
information that are being generated is enormous. This course will present and
discuss quantitative methods used in the analysis of several types of data
bases. Topics may include restriction maps, cloning, genome mapping, sequence
assembly, sequency alignment, and trees and sequences.
Course Note: BIO 230ab, BIO 231, or equivalent required;
ordinal grading option only.
Dr. S. Goldie
2.5 credits
Lectures. Two 2-hour sessions each week.
This course is designed to introduce the student to the
methods and growing range of applications of decision analysis,
cost-effectiveness analysis, and benefit-cost analysis in health care
technology assessment, medical decision making, and health resource allocation.
The objectives of the course are: (1) to provide a technical understanding of
the methods used, (2) to give the student an appreciation of the practical problems
in applying these methods to the evaluation of medical procedures and public
health policies, and (3) to give the student an appreciation of the uses and
limitations of these methods in decision making at the levels of national
policy, health care organizations including hospitals and health maintenance
organizations, and individual patient care.
Course Note: Introductory course in probability and
statistics required; BIO 200ab, BIO 201ab, or BIH 203b may be taken
concurrently; introductory economics is recommended but not required.
Dr. J. Hammitt
2.5 credits
Lectures, seminars. Two 2-hour sessions each week.
Topics include: methods and applications of
cost-effectiveness and cost-benefit analysis for health program evaluation,
medical technology assessment, and environmental risk analysis; theoretical
foundations; "shadow" pricing; economic valuation of life saving;
choice of discount rates; cost accounting applied to economic evaluation in
institutional settings; methods for assessing costs of environmental controls;
economic evaluation of biomedical research; health status indices; ethical
issues; and modern critiques.
Course Note: HPB 280b, HPM 286s, HPM 205ab and HPM 206ab, or
signature of instructor required.
Dr. D. Bialek
2.5 credits
Lectures, case studies. Five 2-hour sessions each week.
Summer 2.
This course will expose students to the concepts and
knowledge involved in making strategic use of information technology (IT) in
health care organizations. It will clarify how to establish IT linkages to
business, planning, and governance. In addition it will introduce students to
technology management through the analysis of the lifecycle of IT, IT
architecture, systems integration, and standards. The course focuses on key
health care implications and the impact of IT upon quality, cost, and
operations.
Course Note: Enrollment in the part-time, non-residential
Masters in Health Care Management program required. Ordinal grading option
only.
Dr. M. Pagano
2.5 credits
Lectures, case studies. Weekend sessions. Academic year 2.
This course covers the fundamentals of biostatistics and
epidemiology and addresses their application to the management of health care
quality. The first part of the course reviews basic biostatistical and
epidemiological concepts, using IT-assisted learning techniques. The second
part of the course is even more interactive discussion requiring student
participation, especially drawing on their experiences to incorporate
biostatistics and epidemiology to more effectively manage the processes and
outcomes of health delivery from the standpoint of quality.
Course Note: Enrollment in the part-time, non-residential
Masters in Health Care Management program required. Ordinal grading option
only.
Dr. S. Goldie
2.5 credits
Lectures. Two 2-hour sessions each week.
This course is designed to introduce the student to the
methods and growing range of applications of decision analysis,
cost-effectiveness analysis, and benefit-cost analysis in health care
technology assessment, medical decision making, and health resource allocation.
The objectives of the course are: (1) to provide a technical understanding of
the methods used, (2) to give the student an appreciation of the practical
problems in applying these methods to the evaluation of medical procedures and
public health policies, and (3) to give the student an appreciation of the uses
and limitations of these methods in decision making at the levels of national
policy, health care organizations including hospitals and health maintenance
organizations, and individual patient care.
Course Note: Introductory course in probability and
statistics required; BIO 200ab, BIO 201ab, or BIH 203b may be taken
concurrently; introductory economics is recommended but not required.
Dr. K. Kuntz, Dr. M. Weinstein
2.5 credits
Lectures, seminars. Two 2-hour sessions each week.
An intermediate-level course on methods and health
applications of decision analysis and other modeling techniques. Topics include
Markov models, life expectancy modeling, deterministic and probabilistic
sensitivity analysis, simulation models, ROC analysis and diagnostic technology
assessment, quality of life valuation, multi-attribute utility, and behavioral
decision theory.
Course Note: HPB 280b, HPM 286s, or equivalent introductory
course on decision analysis required; signature of instructor required;
familiarity with matrix algebra and elementary calculus may be helpful but not
required.
Dr. J. Hammitt
5 credits
Lectures. Two 2-hour sessions each week.
Introduces the standard model of decision-making under
uncertainty, its conceptual foundations, challenges, alternatives, and
methodological issues arising from the application of these techniques to
health issues. Topics include von Neumann-Morgenstern and multi-attribute
utility theory, Bayesian statistical decision theory, stochastic dominance, the
value of information, judgment under uncertainty and alternative models of
probability (Dempster-Shafer theory, generalized probability), and decision
making (regret theory, prospect theory, generalized expected utility).
Applications are to preferences for health and aggregation of preferences over
time and across individuals.
Course Note: Prior course work in decision analysis
required.
Dr. J. Nobel
2.5 credits
Lectures. Two 2-hour sessions each week.
This course will explore information systems from the
perspectives of providers, payers, and consumers within the health care
environment. Leading edge technology, systems theory, health care software applications
and health care strategic planning will be described and placed in context by
guest discussants. Topics include computerized patient records, repository
databases, and clinical decision support systems, as well as policy,
regulatory, and related concerns.
Dr.
T. Brennan
1.25
credits
Lectures.
One 1-hour session each week.
This
course is required for all students engaged in studies supported by the
National Institutes of Health, and is open to everyone. The course reviews a
series of ethical issues that arise in the conduct of research. Topics will
include informed consent, disclosure of conflicts of interest, multiple
authorship issues, issues in mentoring, including gender and race-based
discrimination, and the federal oversight process.
Course
Activities: Multiple lecturers will conduct interactive sessions.
Course
Note: Pass/Fail only.
Dr. D. Bates, Dr. G. Kuperman
2.5 credits
Lectures, seminars. Five 2-hour sessions each week.
Medical informatics will address using data from clinical
information systems in performing clinical effectiveness research, including
the strengths and limitations of these data. Major topics will include an
overview of medical informatics; discussion of the nature of computer-based
data including medical vocabularies and obtaining information from clinical
systems; and clinical systems with a focus on clinical decision support and how
to evaluate their impact. Special topics will also be covered including large databases,
the Web, confidentiality-related issues, information retrieval, and patient
computing.
Course Activities: Students will have to write a paper about
a proposed analysis using data from a clinical information system.
Course Note: Ordinal grading only.