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6.7480 | Information Theory: From Coding to Learning (New) | Introduces fundamentals of information theory and its applications to contemporary problems in statistics, machine learning, and computer science. A thorough study of information measures, including Fisher information, f-divergences, their convex duality, and variational characterizations. Covers information-theoretic treatment of inference, hypothesis testing and large deviations, universal compression, channel coding, lossy compression, and strong data-processing inequalities. Methods are applied to deriving PAC-Bayes bounds, GANs, and regret inequalities in machine learning, metric and non-parametric estimation in statistics, communication complexity, and computation with noisy gates in computer science. Fast-paced journey through a recent textbook with the same title. For a communication-focused version, consider 6.7470. | false | Fall | Graduate | 3-0-9 | 6.3700, 6.3800, or 18.05 | null | false | false | false | False | False | False |
6.3700 | Introduction to Probability | An introduction to probability theory, the modeling and analysis of probabilistic systems, and elements of statistical inference. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation, and further topics about random variables. Limit Theorems. Bayesian estimation and hypothesis testing. Elements of classical statistical inference. Bernoulli and Poisson processes. Markov chains. Students taking graduate version complete additional assignments. | true | Fall, Spring | Undergraduate | 4-0-8 | Calculus II (GIR) | null | false | false | true | False | False | False |
6.3702 | Introduction to Probability | An introduction to probability theory, the modeling and analysis of probabilistic systems, and elements of statistical inference. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation, and further topics about random variables. Limit Theorems. Bayesian estimation and hypothesis testing. Elements of classical statistical inference. Bernoulli and Poisson processes. Markov chains. Students taking graduate version complete additional assignments. | true | Fall, Spring | Graduate | 4-0-8 | Calculus II (GIR) | null | false | false | false | False | False | False |
6.3720 | Introduction to Statistical Data Analysis | Introduction to the central concepts and methods of data science with an emphasis on statistical grounding and modern computational capabilities. Covers principles involved in extracting information from data for the purpose of making predictions or decisions, including data exploration, feature selection, model fitting, and performance assessment. Topics include learning of distributions, hypothesis testing (including multiple comparison procedures), linear and nonlinear regression and prediction, classification, time series, uncertainty quantification, model validation, causal inference, optimization, and decisions. Computational case studies and projects drawn from applications in finance, sports, engineering, and machine learning life sciences. Students taking graduate version complete additional assignments. Recommended prerequisite: 18.06. | true | Spring | Undergraduate | 4-0-8 | 6.100A and (6.3700, 6.3800, or 18.600) | null | false | false | false | False | False | False |
6.3722 | Introduction to Statistical Data Analysis | Introduction to the central concepts and methods of data science with an emphasis on statistical grounding and modern computational capabilities. Covers principles involved in extracting information from data for the purpose of making predictions or decisions, including data exploration, feature selection, model fitting, and performance assessment. Topics include learning of distributions, hypothesis testing (including multiple comparison procedures), linear and nonlinear regression and prediction, classification, time series, uncertainty quantification, model validation, causal inference, optimization, and decisions. Computational case studies and projects drawn from applications in finance, sports, engineering, and machine learning life sciences. Students taking graduate version complete additional assignments. Recommended prerequisite: 18.06. | true | Spring | Graduate | 4-0-8 | 6.100A and (6.3700, 6.3800, 18.600, or permission of instructor) | null | false | false | false | False | False | False |
6.3730[J] | Statistics, Computation and Applications | Hands-on analysis of data demonstrates the interplay between statistics and computation. Includes four modules, each centered on a specific data set, and introduced by a domain expert. Provides instruction in specific, relevant analysis methods and corresponding algorithmic aspects. Potential modules may include medical data, gene regulation, social networks, finance data (time series), traffic, transportation, weather forecasting, policy, or industrial web applications. Projects address a large-scale data analysis question. Students taking graduate version complete additional assignments. Enrollment limited; priority to Statistics and Data Science minors, and to juniors and seniors. | true | Spring | Undergraduate | 3-1-8 | (6.100B, (18.03, 18.06, or 18.C06), and (6.3700, 6.3800, 14.30, 16.09, or 18.05)) or permission of instructor | IDS.012[J] | false | false | false | False | False | False |
6.3732[J] | Statistics, Computation and Applications | Hands-on analysis of data demonstrates the interplay between statistics and computation. Includes four modules, each centered on a specific data set, and introduced by a domain expert. Provides instruction in specific, relevant analysis methods and corresponding algorithmic aspects. Potential modules may include medical data, gene regulation, social networks, finance data (time series), traffic, transportation, weather forecasting, policy, or industrial web applications. Projects address a large-scale data analysis question. Students taking graduate version complete additional assignments. Limited enrollment; priority to Statistics and Data Science minors and to juniors and seniors. | true | Spring | Graduate | 3-1-8 | (6.100B, (18.03, 18.06, or 18.C06), and (6.3700, 6.3800, 14.30, 16.09, or 18.05)) or permission of instructor | IDS.131[J] | false | false | false | False | False | False |
6.7700[J] | Fundamentals of Probability | Introduction to probability theory. Probability spaces and measures. Discrete and continuous random variables. Conditioning and independence. Multivariate normal distribution. Abstract integration, expectation, and related convergence results. Moment generating and characteristic functions. Bernoulli and Poisson process. Finite-state Markov chains. Convergence notions and their relations. Limit theorems. Familiarity with elementary probability and real analysis is desirable. | true | Fall | Graduate | 4-0-8 | Calculus II (GIR) | 15.085[J] | false | false | false | False | False | False |
6.7710 | Discrete Stochastic Processes | Review of probability and laws of large numbers; Poisson counting process and renewal processes; Markov chains (including Markov decision theory), branching processes, birth-death processes, and semi-Markov processes; continuous-time Markov chains and reversibility; random walks, martingales, and large deviations; applications from queueing, communication, control, and operations research. | true | Spring | Graduate | 4-0-8 | 6.3702 or 18.204 | null | false | false | false | False | False | False |
6.7720[J] | Discrete Probability and Stochastic Processes | Provides an introduction to tools used for probabilistic reasoning in the context of discrete systems and processes. Tools such as the probabilistic method, first and second moment method, martingales, concentration and correlation inequalities, theory of random graphs, weak convergence, random walks and Brownian motion, branching processes, Markov chains, Markov random fields, correlation decay method, isoperimetry, coupling, influences and other basic tools of modern research in probability will be presented. Algorithmic aspects and connections to statistics and machine learning will be emphasized. | true | Spring | Graduate | 3-0-9 | 6.3702, 6.7700, 18.100A, 18.100B, or 18.100Q | 15.070[J], 18.619[J] | false | false | false | False | False | False |
6.3800 | Introduction to Inference | Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure, including graphical and neural network representations. Belief propagation, decision-making, classification, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Computational laboratory component explores the concepts introduced in class in the context of contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results. | true | Fall | Undergraduate | 4-4-4 | Calculus II (GIR) or permission of instructor | null | true | false | false | False | False | False |
6.7800 | Inference and Information | Introduction to principles of Bayesian and non-Bayesian statistical inference and its information theoretic foundations. Hypothesis testing and parameter estimation, sufficient statistics, exponential families. Loss functions, information measures, model capacity, and information geometry. Variational inference and EM algorithm; MCMC and other Monte Carlo methods. Asymptotic analysis and large deviations theory; universal inference and learning. Selected topics such as representation learning, score-matching, diffusion, and nonparametric statistics. | true | Spring | Graduate | 4-0-8 | 6.3700, 6.3800, or 6.7700 | null | false | false | false | False | False | False |
6.7810 | Algorithms for Inference | Introduction to computational aspects of statistical inference via probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Sampling methods; Glauber dynamics and mixing time analysis. Parameter structure learning for graphical models; Baum-Welch and Chow-Liu algorithms. Selected topics such as causal inference, particle filtering, restricted Boltzmann machines, and graph neural networks. | true | Fall | Graduate | 4-0-8 | 18.06 and (6.3700, 6.3800, or 6.7700) | null | false | false | false | False | False | False |
6.7820[J] | Graphical Models: A Geometric, Algebraic, and Combinatorial Perspective | Provides instruction in the geometric, algebraic and combinatorial perspective on graphical models. Presents methods for learning the underlying graph and inferring its parameters. Topics include exponential families, duality theory, conic duality, polyhedral geometry, undirected graphical models, Bayesian networks, Markov properties, total positivity of distributions, hidden variables, and tensor decompositions. | true | Fall | Graduate | 3-0-9 | 6.3702 and 18.06 | IDS.136[J] | false | false | false | False | False | False |
6.7830 | Bayesian Modeling and Inference | Covers Bayesian modeling and inference at an advanced graduate level. Topics include de Finetti's theorem, decision theory, approximate inference (modern approaches and analysis of Monte Carlo, variational inference, etc.), hierarchical modeling, (continuous and discrete) nonparametric Bayesian approaches, sensitivity and robustness, and evaluation. | true | Spring | Graduate | 3-0-9 | 6.7700 and 6.7900 | null | false | false | false | False | False | False |
6.3900 | Introduction to Machine Learning | Introduction to the principles and algorithms of machine learning from an optimization perspective. Topics include linear and non-linear models for supervised, unsupervised, and reinforcement learning, with a focus on gradient-based methods and neural-network architectures. Previous experience with algorithms may be helpful. | true | Fall, Spring | Undergraduate | 4-0-8 | (6.1010 or 6.1210) and (18.03, 18.06, 18.700, or 18.C06) | null | false | false | false | False | False | False |
6.3950 | AI, Decision Making, and Society | Introduction to fundamentals of modern data-driven decision-making frameworks, such as causal inference and hypothesis testing in statistics as well as supervised and reinforcement learning in machine learning. Explores how these frameworks are being applied in various societal contexts, including criminal justice, healthcare, finance, and social media. Emphasis on pinpointing the non-obvious interactions, undesirable feedback loops, and unintended consequences that arise in such settings. Enables students to develop their own principled perspective on the interface of data-driven decision making and society. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 4-0-8 | None. Coreq: 6.1200, 6.3700, 6.3800, 18.05, or 18.600 | null | false | false | false | False | False | False |
6.3952 | AI, Decision Making, and Society | Introduction to fundamentals of modern data-driven decision-making frameworks, such as causal inference and hypothesis testing in statistics as well as supervised and reinforcement learning in machine learning. Explores how these frameworks are being applied in various societal contexts, including criminal justice, healthcare, finance, and social media. Emphasis on pinpointing the non-obvious interactions, undesirable feedback loops, and unintended consequences that arise in such settings. Enables students to develop their own principled perspective on the interface of data-driven decision making and society. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 4-0-8 | None. Coreq: 6.1200, 6.3700, 6.3800, or 18.05 | null | false | false | false | False | False | False |
6.7900 | Machine Learning | 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. | true | Fall | Graduate | 3-0-9 | 18.06 and (6.3700, 6.3800, or 18.600) | null | false | false | false | False | False | False |
6.7910[J] | Statistical Learning Theory and Applications | Covers foundations and recent advances in statistical machine learning theory, with the dual goals of providing students with the theoretical knowledge to use machine learning and preparing more advanced students to contribute to progress in the field. The content is roughly divided into three parts. The first part is about classical regularization, margin, stochastic gradient methods, overparametrization, implicit regularization, and stability. The second part is about deep networks: approximation and optimization theory plus roots of generalization. The third part is about the connections between learning theory and the brain. Occasional talks by leading researchers on advanced research topics. Emphasis on current research topics. | true | Fall | Graduate | 3-0-9 | 6.3700, 6.7900, 18.06, or permission of instructor | 9.520[J] | false | false | false | False | False | False |
6.7920[J] | Reinforcement Learning: Foundations and Methods | Examines reinforcement learning (RL) as a methodology for approximately solving sequential decision-making under uncertainty, with foundations in optimal control and machine learning. Provides a mathematical introduction to RL, including dynamic programming, statistical, and empirical perspectives, and special topics. Core topics include: dynamic programming, special structures, finite and infinite horizon Markov Decision Processes, value and policy iteration, Monte Carlo methods, temporal differences, Q-learning, stochastic approximation, and bandits. Also covers approximate dynamic programming, including value-based methods and policy space methods. Applications and examples drawn from diverse domains. Focus is mathematical, but is supplemented with computational exercises. An analysis prerequisite is suggested but not required; mathematical maturity is necessary. | true | Fall | Graduate | 4-0-8 | 6.3700 or permission of instructor | 1.127[J], IDS.140[J] | false | false | false | False | False | False |
6.7930[J] | Machine Learning for Healthcare | 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. | true | Spring | Graduate | 4-0-8 | 6.3900, 6.4100, 6.7810, 6.7900, 6.8611, or 9.520 | HST.956[J] | false | false | false | False | False | False |
6.7940 | Dynamic Programming and Reinforcement Learning | Dynamic programming as a unifying framework for sequential decision-making under uncertainty, Markov decision problems, and stochastic control. Perfect and imperfect state information models. Finite horizon and infinite horizon problems, including discounted and average cost formulations. Value and policy iteration. Suboptimal methods. Approximate dynamic programming for large-scale problems, and reinforcement learning. Applications and examples drawn from diverse domains. While an analysis prerequisite is not required, mathematical maturity is necessary. | true | Spring | Graduate | 4-0-8 | 6.3700 or 18.600 | null | false | false | false | False | False | False |
6.7950 | Advanced Topics in Control | Advanced study of topics in control. Specific focus varies from year to year. | true | Fall | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
6.7960 | Deep Learning (New) | Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics. | true | Fall | Graduate | 3-0-9 | 18.05 and (6.3720, 6.3900, or 6.C01) | null | false | false | false | False | False | False |
6.4100 | Artificial Intelligence | Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals. | true | Fall | Undergraduate | 4-3-5 | 6.100A | null | false | false | false | False | False | False |
6.4102 | Artificial Intelligence | Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 4-3-5 | 6.100A | null | false | false | false | False | False | False |
6.4110 | Representation, Inference, and Reasoning in AI | An introduction to representations and algorithms for artificial intelligence. Topics covered include: constraint satisfaction in discrete and continuous problems, logical representation and inference, Monte Carlo tree search, probabilistic graphical models and inference, planning in discrete and continuous deterministic and probabilistic models including MDPs and POMDPs. | true | Spring | Undergraduate | 3-0-9 | (16.09 and 16.410) or (6.1010, 6.1210, and (6.3700 or 6.3800)) | null | false | false | false | False | False | False |
6.4120[J] | Computational Cognitive Science | Introduction to computational theories of human cognition. Focus on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks covered include Bayesian and hierarchical Bayesian models; probabilistic graphical models; nonparametric statistical models and the Bayesian Occam's razor; sampling algorithms for approximate learning and inference; and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project. | true | Fall | Undergraduate | 3-0-9 | 6.3700, 6.3800, 9.40, 18.05, 6.3900, or permission of instructor | 9.66[J] | false | false | false | False | False | False |
6.4130[J] | Principles of Autonomy and Decision Making | Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 4-0-8 | 6.100B, 6.1010, 6.9080, or permission of instructor | 16.410[J] | false | false | false | False | False | False |
6.4132[J] | Principles of Autonomy and Decision Making | Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | 6.100B, 6.9080, or permission of instructor | 16.413[J] | false | false | false | False | False | False |
6.4150[J] | Artificial Intelligence for Business | Explores how to design and evaluate products and policy based on artificial intelligence. Provides a functional (as opposed to mechanistic) understanding of the emerging technologies underlying AI. Presents AI's opportunities and risks and how to create conditions under which its deployment can succeed. No technical background required. | true | Spring | Graduate | 3-0-6 | null | 15.563[J] | false | false | false | False | False | False |
6.8110[J] | Cognitive Robotics | Highlights algorithms and paradigms for creating human-robot systems that act intelligently and robustly, by reasoning from models of themselves, their counterparts and their world. Examples include space and undersea explorers, cooperative vehicles, manufacturing robot teams and everyday embedded devices. Themes include architectures for goal-directed systems; decision-theoretic programming and robust execution; state-space programming, activity and path planning; risk-bounded programming and risk-bounded planners; self-monitoring and self-diagnosing systems, and human-robot collaboration. Student teams explore recent advances in cognitive robots through delivery of advanced lectures and final projects, in support of a class-wide grand challenge. Enrollment may be limited. | true | Spring | Graduate | 3-0-9 | (6.4100 or 16.413) and (6.1200, 6.3700, or 16.09) | 16.412[J] | false | false | false | False | False | False |
6.8120 | Tissues vs. Silicon in Machine Learning (New) | Examines how brain neural circuits and function can affect the design of machine learning hardware and software, and vice versa. Builds an understanding of how similar and different the computational approaches of the two are, and what can be deduced from one area about the other. Studies the relationship between brain neural circuits and machine learning design, exploring how insights from one can inform the other. Compares biological concepts like neurons, connectomes, and non-backpropagation learning with artificial neural network hardware and software designs, scaling laws, and state-of-the-art optimization techniques. | false | Spring | Graduate | 3-0-9 | 6.3900 | null | false | false | false | False | False | False |
6.4200[J] | Robotics: Science and Systems | Presents concepts, principles, and algorithmic foundations for robots and autonomous vehicles operating in the physical world. Topics include sensing, kinematics and dynamics, state estimation, computer vision, perception, learning, control, motion planning, and embedded system development. Students design and implement advanced algorithms on complex robotic platforms capable of agile autonomous navigation and real-time interaction with the physical word. Students engage in extensive written and oral communication exercises. Enrollment limited. | true | Spring | Undergraduate | 2-6-4 | ((1.00 or 6.100A) and (2.003, 6.1010, 6.1210, or 16.06)) or permission of instructor | 2.124[J], 16.405[J] | true | false | false | False | False | False |
6.4210 | Robotic Manipulation | Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based). Students taking graduate version complete additional assignments. Students engage in extensive written and oral communication exercises. | true | Fall | Undergraduate | 4-2-9 | (6.100A and 6.3900) or permission of instructor | null | false | false | false | False | False | False |
6.4212 | Robotic Manipulation | Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | (6.100A and 6.3900) or permission of instructor | null | false | false | false | False | False | False |
6.8200 | Sensorimotor Learning | Provides an in-depth view of the state-of-the-art learning methods for control and the know-how of applying these techniques. Topics span reinforcement learning, self-supervised learning, imitation learning, model-based learning, and advanced deep learning architectures, and specific machine learning challenges unique to building sensorimotor systems. Discusses how to identify if learning-based control can help solve a particular problem, how to formulate the problem in the learning framework, and what algorithm to use. Applications of algorithms in robotics, logistics, recommendation systems, playing games, and other control domains covered. Instruction involves two lectures a week, practical experience through exercises, discussion of current research directions, and a group project. | true | Spring | Graduate | 3-0-9 | 6.3900 or 6.7900 | null | false | false | false | False | False | False |
6.8210 | Underactuated Robotics | Covers nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines. | true | Spring | Graduate | 3-0-9 | 18.03 and 18.06 | null | false | false | false | False | False | False |
6.4400 | Computer Graphics | Introduction to computer graphics algorithms, software and hardware. Topics include ray tracing, the graphics pipeline, transformations, texture mapping, shadows, sampling, global illumination, splines, animation and color. | true | Fall | Undergraduate | 3-0-9 | 6.1010 and (18.06 or 18.C06) | null | false | false | false | False | False | False |
6.4420[J] | Computational Design and Fabrication | Introduces computational aspects of computer-aided design and manufacturing. Explores relevant methods in the context of additive manufacturing (e.g., 3D printing). Topics include computer graphics (geometry modeling, solid modeling, procedural modeling), physically-based simulation (kinematics, finite element method), 3D scanning/geometry processing, and an overview of 3D fabrication methods. Exposes students to the latest research in computational fabrication. Students taking the graduate version complete additional assignments. | true | Spring | Undergraduate | 3-0-9 | Calculus II (GIR) and (6.1010 or permission of instructor) | 2.0911[J] | false | false | false | False | False | False |
6.8410 | Shape Analysis | Introduces mathematical, algorithmic, and statistical tools needed to analyze geometric data and to apply geometric techniques to data analysis, with applications to fields such as computer graphics, machine learning, computer vision, medical imaging, and architecture. Potential topics include applied introduction to differential geometry, discrete notions of curvature, metric embedding, geometric PDE via the finite element method (FEM) and discrete exterior calculus (DEC),; computational spectral geometry and relationship to graph-based learning, correspondence and mapping, level set method, descriptor, shape collections, optimal transport, and vector field design. | true | Spring | Graduate | 3-0-9 | Calculus II (GIR), 18.06, and (6.8300 or 6.4400) | null | false | false | false | False | False | False |
6.8420 | Computational Design and Fabrication | Introduces computational aspects of computer-aided design and manufacturing. Explores relevant methods in the context of additive manufacturing (e.g., 3D printing). Topics include computer graphics (geometry modeling, solid modeling, procedural modeling), physically-based simulation (kinematics, finite element method), 3D scanning/geometry processing, and an overview of 3D fabrication methods. Exposes students to the latest research in computational fabrication. Students taking graduate version complete additional assignments. | true | Spring | Graduate | 3-0-9 | Calculus II (GIR) and (6.1010 or permission of instructor) | null | false | false | false | False | False | False |
6.4500 | Design for the Web: Languages and User Interfaces (New) | Instruction in the principles and technologies for designing usable user interfaces for Web applications. Focuses on the key principles and methods of user interface design, including learnability, efficiency, safety, prototyping, and user testing. Provides instruction in the core web languages of HTML, CSS, and Javascript, their different roles, and the rationales for the widely varying designs. These languages are used to create usable web interfaces and applications. Covers fundamentals of graphic design theory, as design and usability go hand in hand. | true | Spring | Undergraduate | 2-2-8 | None. Coreq: 6.1010 | null | false | false | false | False | False | False |
6.4510 | Engineering Interactive Technologies | Provides instruction in building cutting-edge interactive technologies, explains the underlying engineering concepts, and shows how those technologies evolved over time. Students use a studio format (i.e., extended periods of time) for constructing software and hardware prototypes. Topics include interactive technologies, such as multi-touch, augmented reality, haptics, wearables, and shape-changing interfaces. In a group project, students build their own interactive hardware/software prototypes and present them in a live demo at the end of term. Enrollment may be limited. | true | Fall | Undergraduate | 1-5-6 | 6.1020, 6.2050, 6.2060, 6.9010, or permission of instructor | null | false | false | false | False | False | False |
6.4530[J] | Principles and Practice of Assistive Technology | Students work closely with people with disabilities to develop assistive and adaptive technologies that help them live more independently. Covers design methods and problem-solving strategies; human factors; human-machine interfaces; community perspectives; social and ethical aspects; and assistive technology for motor, cognitive, perceptual, and age-related impairments. Prior knowledge of one or more of the following areas useful: software; electronics; human-computer interaction; cognitive science; mechanical engineering; control; or MIT hobby shop, MIT PSC, or other relevant independent project experience. Enrollment may be limited. | true | Fall | Undergraduate | 2-4-6 | Permission of instructor | 2.78[J], HST.420[J] | false | false | false | False | False | False |
6.4550[J] | Interactive Music Systems | Explores audio synthesis, musical structure, human computer interaction (HCI), and visual presentation for the creation of interactive musical experiences. Topics include audio synthesis; mixing and looping; MIDI sequencing; generative composition; motion sensors; music games; and graphics for UI, visualization, and aesthetics. Includes weekly programming assignments in python. Teams build an original, dynamic, and engaging interactive music system for their final project. Students taking graduate version complete different assignments. Limited to 36. | true | Fall, Spring | Undergraduate | 3-0-9 | (6.1010 and 21M.301) or permission of instructor | 21M.385[J] | false | false | false | False | Arts | False |
6.4570[J] | Creating Video Games | Introduces students to the complexities of working in small, multidisciplinary teams to develop video games. Covers creative design and production methods, stressing design iteration and regular testing across all aspects of game development (design, visual arts, music, fiction, and programming). Assumes a familiarity with current video games, and the ability to discuss games critically. Previous experience in audio design, visual arts, or project management recommended. Limited to 36. | true | Fall | Undergraduate | 3-3-6 | 6.100A or CMS.301 | CMS.611[J] | false | false | false | False | Arts | False |
6.4590[J] | Foundations of Information Policy | 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 include 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. Instruction and practice in oral and written communication provided. Students taking graduate version complete additional assignments. Enrollment limited. | false | Fall | Undergraduate | 3-0-9 | Permission of instructor | STS.085[J] | false | false | false | False | Social Sciences | False |
6.8510 | Intelligent Multimodal User Interfaces | Implementation and evaluation of intelligent multi-modal user interfaces, taught from a combination of hands-on exercises and papers from the original literature. Topics include basic technologies for handling speech, vision, pen-based interaction, and other modalities, as well as various techniques for combining modalities. Substantial readings and a term project, where students build a program that illustrates one or more of the themes of the course. | true | Spring | Graduate | 3-0-9 | (6.1020 and 6.4100) or permission of instructor | null | false | false | false | False | False | False |
6.8530 | Interactive Data Visualization | Interactive visualization provides a means of making sense of a world awash in data. Covers the techniques and algorithms for creating effective visualizations, using principles from graphic design, perceptual psychology, and cognitive science. Short assignments build familiarity with the data analysis and visualization design process, and a final project provides experience designing, implementing, and deploying an explanatory narrative visualization or visual analysis tool to address a concrete challenge. | true | Spring | Graduate | 3-0-9 | 6.1020 | null | false | false | false | False | False | False |
6.4710[J] | Evolutionary Biology: Concepts, Models and Computation | Explores and illustrates how evolution explains biology, with an emphasis on computational model building for analyzing evolutionary data. Covers key concepts of biological evolution, including adaptive evolution, neutral evolution, evolution of sex, genomic conflict, speciation, phylogeny and comparative methods, life's history, coevolution, human evolution, and evolution of disease. | true | Spring | Undergraduate | 3-0-9 | (6.100A and 7.03) or permission of instructor | 7.33[J] | false | false | false | False | False | False |
6.8700[J] | Advanced Computational Biology: Genomes, Networks, Evolution | See description for 6.8701. Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication. | true | Fall | Graduate | 4-0-8 | (Biology (GIR), 6.1210, and 6.3700) or permission of instructor | HST.507[J] | false | false | false | False | False | False |
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. | true | Fall | Undergraduate | 3-0-9 | (Biology (GIR), 6.1210, and 6.3700) or permission of instructor | null | false | false | false | False | False | False |
6.8710[J] | Computational Systems Biology: Deep Learning in the Life Sciences | Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments. | false | Spring | Graduate | 3-0-9 | Biology (GIR) and (6.3700 or 18.600) | HST.506[J] | false | false | false | False | False | False |
6.8711[J] | Computational Systems Biology: Deep Learning in the Life Sciences | Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments. | false | Spring | Undergraduate | 3-0-9 | (6.100B and 7.05) or permission of instructor | 20.390[J] | false | false | false | False | False | False |
6.8720[J] | Principles of Synthetic Biology | Introduces the basics of synthetic biology, including quantitative cellular network characterization and modeling. Considers the discovery and genetic factoring of useful cellular activities into reusable functions for design. Emphasizes the principles of biomolecular system design and diagnosis of designed systems. Illustrates cutting-edge applications in synthetic biology and enhances skills in analysis and design of synthetic biological applications. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | null | 20.405[J] | false | false | false | False | False | False |
6.8721[J] | Principles of Synthetic Biology | Introduces the basics of synthetic biology, including quantitative cellular network characterization and modeling. Considers the discovery and genetic factoring of useful cellular activities into reusable functions for design. Emphasizes the principles of biomolecular system design and diagnosis of designed systems. Illustrates cutting-edge applications in synthetic biology and enhances skills in analysis and design of synthetic biological applications. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 3-0-9 | null | 20.305[J] | false | false | false | False | False | False |
6.4800[J] | Biomedical Systems: Modeling and Inference | Medically motivated examples of problems in human health that engage students in systems modeling, signal analysis and inference, and design. Content draws on two domains, first by establishing a model of the human cardiovascular system with signal analysis and inference applications of electrocardiograms in health and disease. In a second topic, medical imaging by MRI is motivated by examples of common clinical decision making, followed by laboratory work with technology and instrumentation with the functionality of commercial diagnostic scanners. Students apply concepts from lectures in labs for data collection for image reconstruction, image analysis, and inference by their own design. Labs further include kits for interactive and portable low-cost devices that can be assembled by the students to demonstrate fundamental building blocks of an MRI system. | true | Fall | Undergraduate | 4-4-4 | (6.3100 and (18.06 or 18.C06)) or permission of instructor | 22.54[J] | false | false | false | False | False | False |
6.4810[J] | Cellular Neurophysiology and Computing | Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Preference to juniors and seniors. | true | Spring | Undergraduate | 5-2-5 | (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor | 2.791[J], 9.21[J], 20.370[J] | false | false | false | False | False | False |
6.4812[J] | Cellular Neurophysiology and Computing | Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. | true | Spring | Graduate | 5-2-5 | (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor | 2.794[J], 9.021[J], 20.470[J], HST.541[J] | false | false | false | False | False | False |
6.4820[J] | Quantitative and Clinical Physiology | Application of the principles of energy and mass flow to major human organ systems. Anatomical, physiological and clinical features of the cardiovascular, respiratory and renal systems. Mechanisms of regulation and homeostasis. Systems, features and devices that are most illuminated by the methods of physical sciences and engineering models. Required laboratory work includes animal studies. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 4-2-6 | Physics II (GIR), 18.03, or permission of instructor | 2.792[J], HST.542[J] | false | false | false | False | False | False |
6.4822[J] | Quantitative and Clinical Physiology | Application of the principles of energy and mass flow to major human organ systems. Anatomical, physiological and clinical features of the cardiovascular, respiratory and renal systems. Mechanisms of regulation and homeostasis. Systems, features and devices that are most illuminated by the methods of physical sciences and engineering models. Required laboratory work includes animal studies. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 4-2-6 | 6.4810 and (2.006 or 6.2300) | 2.796[J], 16.426[J] | false | false | false | False | False | False |
6.4830[J] | Fields, Forces and Flows in Biological Systems | Introduction to electric fields, fluid flows, transport phenomena and their application to biological systems. Flux and continuity laws, Maxwell's equations, electro-quasistatics, electro-chemical-mechanical driving forces, conservation of mass and momentum, Navier-Stokes flows, and electrokinetics. Applications include biomolecular transport in tissues, electrophoresis, and microfluidics. | true | Spring | Undergraduate | 4-0-8 | Biology (GIR), Physics II (GIR), and 18.03 | 2.793[J], 20.330[J] | false | false | false | False | False | False |
6.4832[J] | Fields, Forces, and Flows in Biological Systems | Molecular diffusion, diffusion-reaction, conduction, convection in biological systems; fields in heterogeneous media; electrical double layers; Maxwell stress tensor, electrical forces in physiological systems. Fluid and solid continua: equations of motion useful for porous, hydrated biological tissues. Case studies of membrane transport, electrode interfaces, electrical, mechanical, and chemical transduction in tissues, convective-diffusion/reaction, electrophoretic, electroosmotic flows in tissues/MEMs, and ECG. Electromechanical and physicochemical interactions in cells and biomaterials; musculoskeletal, cardiovascular, and other biological and clinical examples. Prior undergraduate coursework in transport recommended. | true | Fall | Graduate | 3-0-9 | Permission of instructor | 2.795[J], 10.539[J], 20.430[J] | false | false | false | False | False | False |
6.4840[J] | Molecular, Cellular, and Tissue Biomechanics | Develops and applies scaling laws and the methods of continuum mechanics to biomechanical phenomena over a range of length scales. Topics include structure of tissues and the molecular basis for macroscopic properties; chemical and electrical effects on mechanical behavior; cell mechanics, motility and adhesion; biomembranes; biomolecular mechanics and molecular motors. Experimental methods for probing structures at the tissue, cellular, and molecular levels. Students taking graduate version complete additional assignments. | true | Spring | Undergraduate | 4-0-8 | Biology (GIR) and 18.03 | 2.797[J], 3.053[J], 20.310[J] | false | false | false | False | False | False |
6.4842[J] | Molecular, Cellular, and Tissue Biomechanics | Develops and applies scaling laws and the methods of continuum mechanics to biomechanical phenomena over a range of length scales. Topics include structure of tissues and the molecular basis for macroscopic properties; chemical and electrical effects on mechanical behavior; cell mechanics, motility and adhesion; biomembranes; biomolecular mechanics and molecular motors. Experimental methods for probing structures at the tissue, cellular, and molecular levels. Students taking graduate version complete additional assignments. | true | Spring | Graduate | 3-0-9 | Biology (GIR) and 18.03 | 2.798[J], 3.971[J], 10.537[J], 20.410[J] | false | false | false | False | False | False |
6.4860[J] | Medical Device Design | Provides an intense project-based learning experience around the design of medical devices with foci ranging from mechanical to electro mechanical to electronics. Projects motivated by real-world clinical challenges provided by sponsors and clinicians who also help mentor teams. Covers the design process, project management, and fundamentals of mechanical and electrical circuit and sensor design. Students work in small teams to execute a substantial term project, with emphasis placed upon developing creative designs -- via a deterministic design process -- that are developed and optimized using analytical techniques. Includes mandatory lab. Instruction and practice in written and oral communication provided. Students taking graduate version complete additional assignments. Enrollment limited. | true | Spring | Undergraduate | 3-3-6 | 2.008, 6.2040, 6.2050, 6.2060, 22.071, or permission of instructor | 2.750[J] | false | false | false | False | False | False |
6.4861[J] | Medical Device Design | Provides an intense project-based learning experience around the design of medical devices with foci ranging from mechanical to electro mechanical to electronics. Projects motivated by real-world clinical challenges provided by sponsors and clinicians who also help mentor teams. Covers the design process, project management, and fundamentals of mechanical and electrical circuit and sensor design. Students work in small teams to execute a substantial term project, with emphasis placed upon developing creative designs — via a deterministic design process — that are developed and optimized using analytical techniques. Includes mandatory lab. Instruction and practice in written and oral communication provided. Students taking graduate version complete additional assignments. Enrollment limited. | true | Spring | Graduate | 3-3-6 | 2.008, 6.2040, 6.2050, 6.2060, 22.071, or permission of instructor | 2.75[J], HST.552[J] | false | false | false | False | False | False |
6.4880[J] | Biological Circuit Engineering Laboratory | Students assemble individual genes and regulatory elements into larger-scale circuits; they experimentally characterize these circuits in yeast cells using quantitative techniques, including flow cytometry, and model their results computationally. Emphasizes concepts and techniques to perform independent experimental and computational synthetic biology research. Discusses current literature and ongoing research in the field of synthetic biology. Instruction and practice in oral and written communication provided. Enrollment limited. | true | Spring | Undergraduate | 2-8-2 | Biology (GIR) and Calculus II (GIR) | 20.129[J] | true | false | false | False | False | False |
6.4900 | Introduction to EECS via Medical Technology | Explores biomedical signals generated from electrocardiograms, glucose detectors or ultrasound images, and magnetic resonance images. Topics include physical characterization and modeling of systems in the time and frequency domains; analog and digital signals and noise; basic machine learning including decision trees, clustering, and classification; and introductory machine vision. Labs designed to strengthen background in signal processing and machine learning. Students design and run structured experiments, and develop and test procedures through further experimentation. | true | Spring | Undergraduate | 4-4-4 | Calculus II (GIR) and Physics II (GIR) | null | true | false | false | False | False | False |
6.8800[J] | Biomedical Signal and Image Processing | Fundamentals of digital signal processing with emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. Lab projects, performed in MATLAB, provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Students taking graduate version complete additional assignments. | true | Spring | Graduate | 3-1-8 | (6.3700 and (2.004, 6.3000, 16.002, or 18.085)) or permission of instructor | 16.456[J], HST.582[J] | false | false | false | False | False | False |
6.8801[J] | Biomedical Signal and Image Processing | Fundamentals of digital signal processing with emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. Lab projects, performed in MATLAB, provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Students taking graduate version complete additional assignments. | true | Spring | Undergraduate | 3-1-8 | (6.3700 or permission of instructor) and (2.004, 6.3000, 16.002, or 18.085) | HST.482[J] | false | false | false | False | False | False |
6.8810[J] | Data Acquisition and Image Reconstruction in MRI | Applies analysis of signals and noise in linear systems, sampling, and Fourier properties to magnetic resonance (MR) imaging acquisition and reconstruction. Provides adequate foundation for MR physics to enable study of RF excitation design, efficient Fourier sampling, parallel encoding, reconstruction of non-uniformly sampled data, and the impact of hardware imperfections on reconstruction performance. Surveys active areas of MR research. Assignments include Matlab-based work with real data. Includes visit to a scan site for human MR studies. | true | Fall | Graduate | 3-0-9 | 6.3010 | HST.580[J] | false | false | false | False | False | False |
6.8830[J] | Signal Processing by the Auditory System: Perception | Studies information processing performance of the human auditory system in relation to current physiological knowledge. Examines mathematical models for the quantification of auditory-based behavior and the relation between behavior and peripheral physiology, reflecting the tono-topic organization and stochastic responses of the auditory system. Mathematical models of psychophysical relations, incorporating quantitative knowledge of physiological transformations by the peripheral auditory system. | true | Fall | Graduate | 3-0-9 | (6.3000 and (6.3700 or 6.3702)) or permission of instructor | HST.716[J] | false | false | false | False | False | False |
6.8850[J] | Clinical Data Learning, Visualization, and Deployments (New) | Examines the practical considerations for operationalizing machine learning in healthcare settings, with a focus on robust, private, and fair modeling using real retrospective healthcare data. Explores the pre-modeling creation of dataset pipeline to the post-modeling "implementation science," which addresses how models are incorporated at the point of care. Students complete three homework assignments (one each in machine learning, visualization, and implementation), followed by a project proposal and presentation. Students gain experience in dataset creation and curation, machine learning training, visualization, and deployment considerations that target utility and clinical value. Students partner with computer scientists, engineers, social scientists, and clinicians to better appreciate the multidisciplinary nature of data science. | true | Fall | Graduate | 3-0-9 | (6.7900 and 6.7930) or permission of instructor | HST.953[J] | false | false | false | False | False | False |
6.4300 | Introduction to Computer Vision (New) | Provides an introduction to computer vision, covering topics from early vision to mid- and high-level vision, including low-level image analysis, edge detection, image transformations for image synthesis, methods for 3D scene reconstruction, motion analysis and tracking. Additionally, presents basics of machine learning, convolutional neural networks, and transformers in the context of image and video data for object classification, detection, and segmentation. | true | Spring | Undergraduate | 3-0-9 | 6.3900, (18.06 or 18.C06), and (6.1200, 6.3700, 6.3800, 18.05, or 18.600) | null | false | false | false | False | False | False |
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. | true | Spring | Graduate | 3-0-9 | (6.1200 or 6.3700) and (18.06 or 18.C06) | null | false | false | false | False | False | False |
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. | true | Spring | Undergraduate | 4-0-11 | (6.1200 or 6.3700) and (18.06 or 18.C06) | null | false | false | false | False | False | False |
6.8320 | Advanced Topics in Computer Vision | Seminar exploring advanced research topics in the field of computer vision; focus varies with lecturer. Typically structured around discussion of assigned research papers and presentations by students. Example research areas explored in this seminar include learning in vision, computational imaging techniques, multimodal human-computer interaction, biomedical imaging, representation and estimation methods used in modern computer vision. | true | Fall | Graduate | 3-0-9 | 6.801, 6.8300, or permission of instructor | null | false | false | false | False | False | False |
6.8370 | Advanced Computational Photography | Presents fundamentals and applications of hardware and software techniques used in digital and computational photography, with an emphasis on software methods. Provides sufficient background to implement solutions to photographic challenges and opportunities. Topics include cameras and image formation, image processing and image representations, high-dynamic-range imaging, human visual perception and color, single view 3-D model reconstruction, morphing, data-rich photography, super-resolution, and image-based rendering. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | Calculus II (GIR) and 6.1020 | null | false | false | false | False | False | False |
6.8371 | Digital and Computational Photography | Presents fundamentals and applications of hardware and software techniques used in digital and computational photography, with an emphasis on software methods. Provides sufficient background to implement solutions to photographic challenges and opportunities. Topics include cameras and image formation, image processing and image representations, high-dynamic-range imaging, human visual perception and color, single view 3-D model reconstruction, morphing, data-rich photography, super-resolution, and image-based rendering. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 3-0-9 | Calculus II (GIR) and 6.1010 | null | false | false | false | False | False | False |
6.8610 | Quantitative Methods for Natural Language Processing | Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | 6.3900 and (18.06 or 18.C06) | null | false | false | false | False | False | False |
6.8611 | Quantitative Methods for Natural Language Processing | Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems. Instruction and practice in oral and written communication provided. Students taking graduate version complete additional assignments. | true | Fall | Undergraduate | 4-0-11 | 6.3900 and (18.06 or 18.C06) | null | false | false | false | False | False | False |
6.8620[J] | Spoken Language Processing | Introduces the rapidly developing field of spoken language processing including automatic speech recognition. Topics include acoustic theory of speech production, acoustic-phonetics, signal representation, acoustic and language modeling, search, hidden Markov modeling, neural networks models, end-to-end deep learning models, and other machine learning techniques applied to speech and language processing topics. Lecture material intersperses theory with practice. Includes problem sets, laboratory exercises, and open-ended term project. | true | Spring | Graduate | 3-1-8 | 6.3000 and 6.3900 | HST.728[J] | false | false | false | False | False | False |
6.8630[J] | Natural Language and the Computer Representation of Knowledge | Explores the relationship between the computer representation and acquisition of knowledge and the structure of human language, its acquisition, and hypotheses about its differentiating uniqueness. Emphasizes development of analytical skills necessary to judge the computational implications of grammatical formalisms and their role in connecting human intelligence to computational intelligence. Uses concrete examples to illustrate particular computational issues in this area. | true | Spring | Graduate | 3-3-6 | 6.4100 or permission of instructor | 9.611[J], 24.984[J] | false | false | false | False | False | False |
6.9000 | Engineering for Impact | Students work in teams to engineer hardware/software systems that solve important, challenging real-world problems. In pursuit of these projects, students engage at every step of the full-stack development process, from printed circuit board design to firmware to server to industrial design. Teams design and build functional prototypes of complete hardware/software systems. Grading is based on individual- and team-based elements. Enrollment may be limited due to staffing and space requirements. | true | Spring | Undergraduate | 2-3-7 | 6.1910, 6.2000, and 6.3100 | null | false | false | false | False | False | False |
6.9010 | Introduction to EECS via Interconnected Embedded Systems | Introduction to embedded systems in the context of connected devices, wearables, and the "Internet of Things" (IoT). Topics include microcontrollers, energy utilization, algorithmic efficiency, interfacing with sensors, networking, cryptography, and local versus distributed computation. Students design, make, and program an Internet-connected wearable or handheld device. In the final project, student teams design and demo their own server-connected IoT system. Enrollment limited; preference to first- and second-year students. | true | Spring | Undergraduate | 1-5-6 | 6.100A; Coreq: Physics II (GIR) | null | true | false | false | False | False | False |
6.9020[J] | How to Make (Almost) Anything | Provides a practical hands-on introduction to digital fabrication, including CAD/CAM/CAE, NC machining, 3-D printing and scanning, molding and casting, composites, laser and waterjet cutting, PCB design and fabrication; sensors and actuators; mixed-signal instrumentation, embedded processing, and wired and wireless communications. Develops an understanding of these capabilities through projects using them individually and jointly to create functional systems. | true | Fall | Graduate | 3-9-6 | Permission of instructor | 4.140[J], MAS.863[J] | false | false | false | False | False | False |
6.9030 | Strobe Project Laboratory | Application of electronic flash sources to measurement and photography. First half covers fundamentals of photography and electronic flashes, including experiments on application of electronic flash to photography, stroboscopy, motion analysis, and high-speed videography. Students write four extensive lab reports. In the second half, students work in small groups to select, design, and execute independent projects in measurement or photography that apply learned techniques. Project planning and execution skills are discussed and developed over the term. Students engage in extensive written and oral communication exercises. Enrollment limited. | true | Fall, Spring | Undergraduate | 2-8-2 | Physics II (GIR) or permission of instructor | null | true | false | false | False | False | False |
6.9080 | Introduction to EECS via Robotics | An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Key issues in the design of engineered artifacts operating in the natural world: measuring and modeling system behaviors; assessing errors in sensors and effectors; specifying tasks; designing solutions based on analytical and computational models; planning, executing, and evaluating experimental tests of performance; refining models and designs. Issues addressed in the context of computer programs, control systems, probabilistic inference problems, circuits and transducers, which all play important roles in achieving robust operation of a large variety of engineered systems. | true | Spring | Undergraduate | 2-4-6 | 6.100A or permission of instructor | null | true | false | false | False | False | False |
6.UAR | Seminar in Undergraduate Advanced Research | Instruction in effective undergraduate research, including choosing and developing a research topic, surveying previous work and publications, research topics in EECS and the School of Engineering, industry best practices, design for robustness, technical presentation, authorship and collaboration, and ethics. Students engage in extensive written and oral communication exercises, in the context of an approved advanced research project. A total of 12 units of credit is awarded for completion of the fall and subsequent spring term offerings. Application required; consult EECS SuperUROP website for more information. | true | Fall, Spring | Undergraduate | 2-0-4 | Permission of instructor | null | false | false | false | False | False | False |
6.UAT | Oral Communication | Provides instruction in aspects of effective technical oral presentations and exposure to communication skills useful in a workplace setting. Students create, give and revise a number of presentations of varying length targeting a range of different audiences. Enrollment may be limited. | true | Fall, Spring | Undergraduate | 3-0-6 | null | null | false | false | false | False | False | False |
6.9101[J] | Introduction to Design Thinking and Innovation in Engineering | Introduces students to concepts of design thinking and innovation that can be applied to any engineering discipline. Focuses on introducing an iterative design process, a systems-thinking approach for stakeholder analysis, methods for articulating design concepts, methods for concept selection, and techniques for testing with users. Provides an opportunity for first-year students to explore product or system design and development, and to build their understanding of what it means to lead and coordinate projects in engineering design. Subject can count toward the 6-unit discovery-focused credit limit for first-year students. Enrollment limited to 25; priority to first-year students. | true | Fall, Spring | Undergraduate | 2-0-1 [P/D/F] | null | 2.7231[J], 16.6621[J] | false | false | false | False | False | False |
6.910A | Design Thinking and Innovation Leadership for Engineers | Introductory subject in design thinking and innovation. Develops students' ability to conceive, implement, and evaluate successful projects in any engineering discipline. Lessons focus on an iterative design process, a systems-thinking approach for stakeholder analysis, methods for articulating design concepts, methods for concept selection, and techniques for testing with users. | true | Fall, Spring | Undergraduate | 2-0-1 | null | null | false | false | false | False | False | False |
6.910B | Design Thinking and Innovation Project | Project-based subject. Students employ design-thinking techniques learned in 6.902A to develop a robust speech-recognition application using a web-based platform. Students practice in leadership and teamwork skills as they collaboratively conceive, implement, and iteratively refine their designs based on user feedback. Topics covered include techniques for leading the creative process in teams, the ethics of engineering systems, methods for articulating designs with group collaboration, identifying and reconciling paradoxes of engineering designs, and communicating solution concepts with impact. Students present oral presentations and receive feedback to sharpen their communication skills. | true | Fall, Spring | Undergraduate | 2-0-1 | 6.910A | null | false | false | false | False | False | False |
6.9110 | Engineering Leadership Lab | Advances students' leadership, teamwork, and communication skills through further exposure to leadership frameworks, models, and cases within an engineering context in an interactive, practice-based environment. Students coach others, assess performance, and lead guided reflections on individual and team successes, while discovering opportunities for improvement. Students assist with programmatic planning and implementation of role-play simulations, small group discussions, and performance and peer assessments by and of other students and by instructors. Includes frequent engineering industry-guest participation and involvement. Content is frequently student-led. Second year Gordon Engineering Leadership Program (GEL) Program students register for 6.9130. Preference to students enrolled in the second year of the Gordon-MIT Engineering Leadership Program. | true | Fall, Spring | Undergraduate | 0-2-1 | None. Coreq: 6.9120; or permission of instructor | null | false | false | false | False | False | False |
6.9120 | Engineering Leadership | Exposes students to the models and methods of engineering leadership within the contexts of conceiving, designing, implementing and operating products, processes and systems. Introduces the Capabilities of Effective Engineering Leaders, and models and theories related to the capabilities. Discusses the appropriate times and reasons to use particular models to deliver engineering success. Includes occasional guest speakers or panel discussions. May be repeated for credit once with permission of instructor. Preference to first-year students in the Gordon Engineering Leadership Program. | true | Fall, Spring | Undergraduate | 1-0-2 | None. Coreq: 6.9110; or permission of instructor | null | false | false | false | False | False | False |
6.9130 | Engineering Leadership Lab | Advances students' leadership, teamwork, and communication skills through further exposure to leadership frameworks, models, and cases within an engineering context in an interactive, practice-based environment. Students coach others, assess performance, and lead guided reflections on individual and team successes, while discovering opportunities for improvement. Students assist with programmatic planning and implementation of role-play simulations, small group discussions, and performance and peer assessments by and of other students and by instructors. Includes frequent engineering industry-guest participation and involvement. Content is frequently student-led. Second year Gordon Engineering Leadership Program (GEL) Program students register for 6.9130. Preference to students enrolled in the second year of the Gordon-MIT Engineering Leadership Program. | true | Fall, Spring | Undergraduate | 0-2-4 | 6.910A, 6.9110, 6.9120, or permission of instructor | null | false | false | false | False | False | False |
6.9140 | Project Engineering | Students attend and participate in a four-day off-site workshop covering an introduction to basic principles, methods, and tools for project management in a realistic context. In teams, students create a plan for a project of their choice in one of several areas, including: aircraft modification, factory automation, flood prevention engineering, solar farm engineering, small-business digital transformation/modernization, and disaster response, among others. Develops skills applicable to the planning and management of complex engineering projects. Topics include cost-benefit analysis, resource and cost estimation, and project control and delivery which are practiced during an experiential, team-based activity. Case studies highlight projects in both hardware/software and consumer packaged goods. Preference to students in the Bernard M. Gordon-MIT Engineering Leadership Program. | true | IAP, Spring | Undergraduate | 4-0-0 [P/D/F] | (6.910A and (6.9110 or 6.9120)) or permission of instructor | null | false | false | false | False | False | False |
Subsets and Splits