Analytics Certificate | MIT Sloan
IDS.145J
Data Mining: Finding the Models and Predictions that Create Value
Fall | 6 Cr.
Data Mining: Finding the Models and Predictions that Create Value
Fall
6 Cr.
IDS.145J
Fall H2
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 undergraduate version; consult syllabus or instructor for specific details.
15.068
Statistical Consulting
Spring | 9 Cr.
Statistical Consulting
Spring
9 Cr.
Addresses statistical issues as a consultant would face them: deciphering the client’s question; finding appropriate data; performing a viable analysis; and presenting the results in compelling ways. Real-life cases and examples.
15.071 OR 15.072
Analytics Edge OR Advanced Analytics Edge
Spring | 12 Cr.
Analytics Edge OR Advanced Analytics Edge
Spring
12 Cr.
15.071 Analytics Edge
Spring or Fall
Develops models and tools of data analytics that are used to transform businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, criminal justice, the internet, and beyond. Covers analytics methods such as linear regression, logistic regression, classification trees, random forests, neural networks, text analytics, social network analysis, time series modeling, clustering, and optimization. Uses mostly R programming language with some Python. Includes team projects. Meets with 15.0711 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
OR
15.072 Advanced Analytics Edge
Fall
More advanced version of 15.071 introduces core methods of business analytics, their algorithmic implementations and their applications to various domains of management and public policy. Spans descriptive analytics (e.g., clustering, dimensionality reduction), predictive analytics (e.g., linear/logistic regression, classification and regression trees, random forests, boosting deep learning) and prescriptive analytics (e.g., optimization). Presents analytics algorithms, and their implementations in data science. Includes case studies in e-commerce, transportation, energy, healthcare, social media, sports, the internet, and beyond. Uses the R and Julia programming languages. Includes team projects. Preference to Sloan Master of Business Analytics students.
15.077J
IDS.147J
Statistical Learning and Data science
Spring | 12 Cr.
Statistical Learning and Data science
Spring
12 Cr.
IDS.147J
Advanced introduction to theory and application of statistics, data-mining and machine learning using techniques from management science, marketing, finance, consulting, and bioinformatics. Covers bootstrap theory of estimation, testing, nonparametric statistics, analysis of variance, experimental design, categorical data analysis, regression analysis, MCMC, and Bayesian methods. Focuses on data mining, supervised learning, and multivariate analysis. Topics chosen from logistic regression; principal components and dimension reduction; discrimination and classification analysis, trees (CART), partial least squares, nearest neighbors, regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software R, Python, and MATLAB. Grading based on homework, cases, and a term project.
15.083
Integer Optimization
Spring | 12 Cr.
Integer Optimization
Spring
12 Cr.
In-depth treatment of the modern theory of integer programming and combinatorial optimization, emphasizing geometry, duality, and algorithms. Topics include formulating problems in integer variables, enhancement of formulations, ideal formulations, integer programming duality, linear and semidefinite relaxations, lattices and their applications, the geometry of integer programming, primal methods, cutting plane methods, connections with algebraic geometry, computational complexity, approximation algorithms, heuristic and enumerative algorithms, mixed integer programming and solutions of large-scale problems.
15.093J
6.7200J/IDS.200J
Optimization Methods
Fall | 12 Cr.
Optimization Methods
Fall
12 Cr.
6.7200J/IDS.200J
Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. Emphasizes 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. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
15.094J
1.142J
Robust Modeling, Optimization & Computation
Spring | 12 Cr.
Robust Modeling, Optimization & Computation
Spring
12 Cr.
1.142J
Introduces modern robust optimization, including theory, applications, and computation. Presents formulations and their connection to probability, information and risk theory for conic optimization (linear, second-order, and semidefinite cones) and integer optimization. Application domains include analysis and optimization of stochastic networks, optimal mechanism design, network information theory, transportation, pattern classification, structural and engineering design, and financial engineering. Students formulate and solve a problem aligned with their interests in a final project.
15.095
Machine Learning Under an Optimization Lens
Fall | 12 Cr.
Machine Learning Under an Optimization Lens
Fall
12 Cr.
Develops algorithms for central problems in machine learning from a modern optimization perspective. Topics include sparse, convex, robust and median regression; an algorithmic framework for regression; optimal classification and regression trees, and their relationship with neural networks; how to transform predictive algorithms to prescriptive algorithms; optimal prescriptive trees; and robust classification. Also covers design of experiments, missing data imputations, mixture of Gaussian models, exact bootstrap, and sparse matrix estimation, including principal component analysis, factor analysis, inverse co-variance matrix estimation, and matrix completion.
15.727
The Analytics Edge (EMBA only)
Spring | 9 Cr.
The Analytics Edge (EMBA only)
Spring
9 Cr.
Introduces modern analytics methods (data mining and optimization), starting with real-world problems where analytics have made a material difference. Modern data mining methods include clustering, classification, logistic regression, CART, random forest methods, and association rules. Modern optimization methods include robust, adaptive and dynamic optimization. Applications include health care, hospital operations, finance, energy, security, internet, and demand modeling. Uses R programming language for data mining and ROME for robust optimization. Restricted to Exeuctive MBA students.
15.729
Leadership: Quantitative and Qualitative Approaches (LQ^2) – (EMBA and SF MBA only)
Spring | 3 Cr.
Leadership: Quantitative and Qualitative Approaches (LQ^2) – (EMBA and SF MBA only)
Spring
3 Cr.
IAP
Uses interdisciplinary approaches and real-world examples to show how analytics inform organizational change. Takes into account the human and cultural components of organizations. Restricted to Executive MBA and Sloan Fellow MBA students.
15.S04
SSIM: Hands-On Deep Learning
Spring | 6 Cr.
SSIM: Hands-On Deep Learning
Spring
6 Cr.
Spring H4
Opportunity for group study by graduate students on current topics related to management not otherwise included in curriculum.
15.S08
SSIM: Optimization of Energy Systems
Spring | 12 Cr.
SSIM: Optimization of Energy Systems
Spring
12 Cr.
Opportunity for group study by graduate students on current topics related to management not otherwise included in curriculum.
15.S51
Innovation Through Analytics and Sensing in Food and Agriculture Systems (EMBA and SF MBA only)
Winter | 3 Cr.
Innovation Through Analytics and Sensing in Food and Agriculture Systems (EMBA and SF MBA only)
Winter
3 Cr.
IAP 2022 and 2023
Group study of current topics related to management not otherwise included in curriculum.
6.7900
Machine Learning
Fall | 12 Cr.
Machine Learning
Fall
12 Cr.
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.3900 or other previous experience in machine learning. Enrollment may be limited.
6.7930J
HST.956J
Machine Learning for Healthcare
Spring | 12 Cr.
Machine Learning for Healthcare
Spring
12 Cr.
HST.956J
Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area, and projects with real clinical data, emphasize subtleties of working with clinical data and translating machine learning into clinical practice. Limited to 55.
6.8300
Advances in Computer Vision
Spring | 12 Cr.
Advances in Computer Vision
Spring
12 Cr.
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.C51
Modeling with Machine Learning: from Algorithms to Applications
Spring | 6 Cr.
Modeling with Machine Learning: from Algorithms to Applications
Spring
6 Cr.
Focuses on modeling with machine learning methods with an eye towards applications in engineering and sciences. Introduction to modern machine learning methods, from supervised to unsupervised models, with an emphasis on newer neural approaches. Emphasis on the understanding of how and why the methods work from the point of view of modeling, and when they are applicable. Using concrete examples, covers formulation of machine learning tasks, adapting and extending methods to given problems, and how the methods can and should be evaluated. Students taking graduate version complete additional assignments. Students cannot receive credit without simultaneous completion of a 6-unit disciplinary module. Enrollment may be limited.