MIT Subject Listing & Schedule
Fall 2024 Search Results

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21 subjects found.

3.041 Computational Materials Design

Systems approach to analysis and control of multilevel materials microstructures employing genomic fundamental databases. Applies quantitative process-structure-property-performance relations in computational parametric design of materials composition under processability constraints to achieve predicted microstructures meeting multiple property objectives established by industry performance requirements. Covers integration of macroscopic process models with microstructural simulation to accelerate materials qualification through component-level process optimization and forecasting of manufacturing variation to efficiently define minimum property design allowables. Case studies of interdisciplinary multiphysics collaborative modeling with applications across materials classes. Students taking graduate version complete additional assignments. 

3.321 Computational Materials Design

Systems approach to analysis and control of multilevel materials microstructures employing genomic fundamental databases. Applies quantitative process-structure-property-performance relations in computational parametric design of materials composition under processability constraints to achieve predicted microstructures meeting multiple property objectives established by industry performance requirements. Covers integration of macroscopic process models with microstructural simulation to accelerate materials qualification through component-level process optimization and forecasting of manufacturing variation to efficiently define minimum property design allowables. Case studies of interdisciplinary multiphysics collaborative modeling with applications across materials classes. Students taking graduate version complete additional assignments. 

6.1040 Software Design

Provides design-focused instruction on how to build complex software applications. Design topics include classic human-computer interaction (HCI) design tactics (need finding, heuristic evaluation, prototyping, user testing), conceptual design (inventing, modeling and evaluating constituent concepts), social and ethical implications, abstract data modeling, and visual design. Implementation topics include reactive front-ends, web services, and databases. Students work both on individual projects and a larger team project in which they design and build full-stack web applications.

6.5830 Database Systems

Topics related to the engineering and design of database systems, including data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel and heterogeneous databases; adaptive databases; trigger systems; pub-sub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semester-long project and paper. Students taking graduate version complete different assignments. Enrollment may be limited.

6.5831 Database Systems

Topics related to the engineering and design of database systems, including data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel and heterogeneous databases; adaptive databases; trigger systems; pub-sub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semester-long project and paper. Students taking graduate version complete different assignments. Enrollment may be limited.

6.8300 Advances in Computer Vision

Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Covers image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.8390. Students taking graduate version complete additional assignments.

6.8301 Advances in Computer Vision

Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Covers image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Includes instruction and practice in written and oral communication. Students taking graduate version complete additional assignments.

6.8701 Computational Biology: Genomes, Networks, Evolution

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

11.205 Introduction to Spatial Analysis and GIS

An introduction to Geographic Information Systems (GIS): a tool for visualizing and analyzing data representing locations and their attributes. GIS is invaluable for planners, scholars, and professionals who shape cities and a political instrument with which activists advocate for change. Class includes exercises to make maps, query databases, and analyze spatial data. Because maps and data are never neutral, the class incorporates discussions of power, ethics, and data throughout as part of a reflective practice. Limited enrollment; preference to first-year MCP students.

11.521 Spatial Database Management and Advanced Geographic Information Systems

Extends the computing and geographic information systems (GIS) skills developed in 11.520 to include spatial data management in client/server environments and advanced GIS techniques. First half covers the content of 11.523, introducing database management concepts, SQL (Structured Query Language), and enterprise-class database management software. Second half explores advanced features and the customization features of GIS software that perform analyses for decision support that go beyond basic thematic mapping. Includes the half-term GIS project of 11.524 that studies a real-world planning issue.

11.523 Fundamentals of Spatial Database Management

Develops technical skills necessary to design, build, and interact with spatial databases using the Structured Query Language (SQL) and its spatial extensions. Provides instruction in writing highly contextual metadata (data biographies). Prepares students to perform database maintenance, modeling, and digitizing tasks, and to critically evaluate and document data sources. Databases are implemented in PostgreSQL and PostGIS; students interface with these using QGIS.

15.062J Data Mining: Finding the Models and Predictions that Create Value

Introduction to data mining, data science, and machine learning for recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, medical databases, etc. Topics include logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software (R and Python). Grading based on homework, cases, and a term project. Expectations and evaluation criteria differ for students taking the undergraduate version; consult syllabus or instructor for specific details.

15.0621 Data Mining: Finding the Models and Predictions that Create Value

Introduction to data mining, data science, and machine learning for recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, medical databases, etc. Topics include logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software (R and Python). Grading based on homework, cases, and a term project. Expectations and evaluation criteria differ for students taking the graduate version; consult syllabus or instructor for specific details.

15.418 Laboratory in Corporate Finance

Introduction to corporate finance. Classroom portion primarily uses case studies to introduce financial analytical tools needed to make real-world value-enhancing business decisions across many industries: how to decide which projects to invest in, how to finance those investments, and how to manage the cash flows of the firm. Laboratory sessions are organized around team valuation projects, such as valuation of an oil field and analysis of a potential merger between two public firms proposed by student teams. Projects require extensive use of financial databases. Laboratory sessions also provide instruction on writing and speaking on financial topics. Meets with 15.402 when offered concurrently.

15.458 Financial Data Science and Computing

Covers methods of managing data and extracting insights from real-world financial sources. Topics include machine learning, natural language processing, predictive analytics, regression methods, and time series analysis. Applications include algorithmic trading, portfolio risk management, high-frequency market microstructure, and option pricing. Studies major sources of financial data, raw data cleaning, data visualization, and data architecture. Provides instruction in tools used in the financial industry to process massive data sets, including SQL, relational and multidimensional databases. Emphasizes computer implementations throughout.

15.561 Digital Revolution: From Foundations to Future Trends

Emphasizes programming in scripting languages (e.g., Python) within the context of emerging trends that underlie current and future uses of digital technologies in business. Provides a solid grasp of programming basics and the foundations of computing. Other topics include web technologies, database systems, digital experimentation (A/B testing), crowdsourcing, digital marketplaces, distributed ledger technologies, and AI.

20.215 Macroepidemiology, Population Genetics, and Stem Cell Biology of Human Clonal Diseases

Studies the logic and technology needed to discover genetic and environmental risks for common human cancers and vascular diseases. Includes an introduction to metakaryotic stem cell biology. Analyzes large, organized historical public health databases using quantitative cascade computer models that include population stratification of stem cell mutation rates in fetal/juvenile tissues and growth rates in preneoplastic colonies and atherosclerotic plaques. Means to test hypotheses (CAST) that certain genes carry mutations conferring risk for common cancers via genetic analyses in large human cohorts. Involves de novo computer modeling of a lifetime disease experience or test of a student-developed hypothesis.

21A.303J The Anthropology of Biology

Applies the tools of anthropology to examine biology in the age of genomics, biotechnological enterprise, biodiversity conservation, pharmaceutical bioprospecting, and synthetic biology. Examines such social concerns such as bioterrorism, genetic modification, and cloning. Offers an anthropological inquiry into how the substances and explanations of biology — ecological, organismic, cellular, molecular, genetic, informatic — are changing. Examines such artifacts as cell lines, biodiversity databases, and artificial life models, and using primary sources in biology, social studies of the life sciences, and literary and cinematic materials, asks how we might answer Erwin Schrodinger's 1944 question, "What Is Life?", today.

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

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.

SCM.264 Databases and Data Analysis for Supply Chain Management

Introduces databases, data analysis, and machine learning topics. Covers data modeling, relational databases, SQL queries, data mining, non-relational databases, and data warehouses. Introduces data analysis tools for visualization, regression, supervised and unsupervised techniques including principal component analysis and clustering. Term project includes implementation of data model, database, visualization and data analysis. SCM.274 meets with SCM.264 but requires fewer assignments and lectures. Restricted to SCM students.

SCM.274 Databases and Data Analysis Topics for Supply Chain Management

Introduces databases, data analysis, and machine learning topics. Covers data modeling, relational databases, SQL queries, data mining, non-relational databases, and data warehouses. Introduces data analysis tools for visualization, regression, supervised and unsupervised techniques including principal component analysis and clustering. Term project includes implementation of data model, database, visualization and data analysis. SCM.274 meets with SCM.264 but requires fewer assignments and lectures. Restricted to SCM students.