MIT Subject Listing & Schedule
IAP/Spring 2021 Search Results

Searched for:

6 subjects found.

1.200[J] Transportation Systems Analysis: Performance and Optimization

()
(Same subject as 11.544[J])
Prereq: 1.010 and permission of instructor
Units: 3-1-8

Problem-motivated introduction to methods, models and tools for the analysis and design of transportation networks including their planning, operations and control. Capacity of critical elements of transportation networks. Traffic flows and deterministic and probabilistic delay models. Formulation of optimization models for planning and scheduling of freight, transit and airline systems, and their solution using software packages. User- and system-optimal traffic assignment. Control of traffic flows on highways, urban grids, and airspace.
C. Wu

1.201[J] Transportation Systems Analysis: Demand and Economics

()
Not offered regularly; consult department
(Same subject as 11.545[J])
Prereq: Permission of instructor
Units: 3-1-8
Subject Cancelled

Covers the key principles governing transportation systems planning and management. Introduces the microeconomic concepts central to transportation systems. Topics include economic theories of the firm, consumer, and market, demand models, discrete choice analysis, cost models and production functions, and pricing theory. Applications to transportation systems - including congestion pricing, technological change, resource allocation, market structure and regulation, revenue forecasting, public and private transportation finance, and project evaluation - cover urban passenger transportation, freight, maritime, aviation, and intelligent transportation systems.
Staff

12.318 Introduction to Atmospheric Data and Large-scale Dynamics

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(Subject meets with 12.818)
Prereq: None. Coreq: 12.390
Units: 3-3-6

Provides a general introduction to meteorological data and analysis techniques, and their use in the MIT Synoptic Laboratory to study the phenomenology and dynamics of large-scale atmospheric flow. Illustrates balance concepts as applied to the dynamics of frontal and synoptic scales, using real-time upper-air and surface station data and gridded analyzed fields. Uses advanced meteorological software packages to access, manipulate, and graphically display the data. Students taking graduate version complete different assignments.
L. Illari

12.818 Introduction to Atmospheric Data and Large-scale Dynamics

()
(Subject meets with 12.318)
Prereq: None. Coreq: 12.800
Units: 3-3-6

Provides a general introduction to meteorological data and analysis techniques, and their use in the MIT Synoptic Laboratory to study the phenomenology and dynamics of large-scale atmospheric flow. Illustrates balance concepts as applied to the dynamics of frontal and synoptic scales, using real-time upper-air and surface station data and gridded analyzed fields. Uses advanced meteorological software packages to access, manipulate, and graphically display the data. Students taking graduate version complete different assignments.
L. Illari

15.0741 Predictive Data Analytics and Statistical Modeling

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Not offered regularly; consult department
Prereq: 6.041B
Units: 4-0-5

Provides a brief review of statistics and regression drawn from advanced topics, such as bootstrap resampling, variable selection, data and regression diagnostics, visualization, and Bayesian and robust methods. Covers data-mining and machine learning, including classification, logistic regression, and clustering. Culminates with time series analysis and forecasting, design of experiments, analysis of variance, and process control. Uses statistical computing systems based on application add-ins and stand-alone packages. Case studies involve finance, management science, consulting, risk management, and engineering systems. Term project required.
Staff

16.470 Statistical Methods in Experimental Design

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Not offered regularly; consult department
Prereq: 16.09, 6.041, or permission of instructor
Units: 3-0-9
Subject Cancelled

Statistically based experimental design inclusive of forming hypotheses, planning and conducting experiments, analyzing data, and interpreting and communicating results. Topics include descriptive statistics, statistical inference, hypothesis testing, parametric and nonparametric statistical analyses, factorial ANOVA, randomized block designs, MANOVA, linear regression, repeated measures models, and application of statistical software packages.
Staff

SCM.256 Data Science and Machine Learning for Supply Chain Management

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Prereq: SCM.254 or permission of instructor
Units: 3-0-9
In person required. Lecture: MW11-12.30 (VIRTUAL) Lab: T EVE (5.30-7 PM) (E40-356) or T EVE (7-8.30 PM) (E40-356) or W EVE (5.30-7 PM) (VIRTUAL)

Introduces data science and machine learning topics in both theory and application. Data science topics include database and API connections, data preparation and manipulation, and data structures. Machine learning topics include model fitting, tuning and prediction, end-to-end problem solving, feature engineering and feature selection, overfitting, generalization, classification, regression, neural networks, dimensionality reduction and clustering. Covers software packages for statistical analysis, data visualization and machine learning. Introduces best practices related to source control, system architecture, cloud computing frameworks and modules, security, emerging financial technologies and software process. Applies teaching examples to logistics, transportation, and supply chain problems. Enrollment limited.
C. Cassa, T. Hall, N. Loehndorf
No textbook information available