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Course 6: Electrical Engineering and Computer Science
IAP/Spring 2024


Artificial Intelligence

6.4100 Artificial Intelligence
______

Undergrad (Fall)
Not offered regularly; consult department
Prereq: 6.100A
Units: 4-3-5
______
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.
Consult Department

6.4102 Artificial Intelligence
______

Graduate (Fall)
Not offered regularly; consult department
Prereq: 6.100A
Units: 4-3-5
______
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.
Consult Department

6.4110 Representation, Inference, and Reasoning in AI
______

Undergrad (Fall)
(Subject meets with 16.420)
Prereq: (16.09 and 16.410) or (6.1010, 6.1210, and (6.3700 or 6.3800))
Units: 3-0-9
______
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.
L. Kaelbling

6.4120[J] Computational Cognitive Science
______

Undergrad (Fall)
(Same subject as 9.66[J])
(Subject meets with 9.660)
Prereq: 6.3700, 6.3800, 9.40, 18.05, 6.3900, or permission of instructor
Units: 3-0-9
______
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.
J. Tenenbaum

6.4130[J] Principles of Autonomy and Decision Making
______

Undergrad (Fall)
(Same subject as 16.410[J])
(Subject meets with 6.4132[J], 16.413[J])
Prereq: 6.100B or 6.9080
Units: 4-0-8
______
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.
B.C. Williams

6.4132[J] Principles of Autonomy and Decision Making
______

Graduate (Fall)
(Same subject as 16.413[J])
(Subject meets with 6.4130[J], 16.410[J])
Prereq: 6.100B, 6.9080, or permission of instructor
Units: 3-0-9
______
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.
B.C. Williams

6.4150[J] Artificial Intelligence for Business
(New)
______

Graduate (Spring)
(Same subject as 15.563[J])
Prereq: None
Units: 3-0-6
URL: https://mraghavan.github.io/files/2024-spring-syllabus.pdf
Lecture: TR2.30-4 (E62-262)
______
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.
M. Raghavan
No required or recommended textbooks

6.8110[J] Cognitive Robotics
______

Graduate (Spring)
(Same subject as 16.412[J])
Prereq: (6.4100 or 16.413) and (6.1200, 6.3700, or 16.09)
Units: 3-0-9
Lecture: MW11-12.30 (32-155)
______
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.
B. C. Williams
No textbook information available

Robotics

6.4200[J] Robotics: Science and Systems
______

Undergrad (Spring) Institute Lab
(Same subject as 2.124[J], 16.405[J])
Prereq: ((1.00 or 6.100A) and (2.003, 6.1010, 6.1210, or 16.06)) or permission of instructor
Units: 2-6-4
Lecture: MWF1 (26-100) Lab: MW3-5 (BEGINS APR 1) (45-230)
______
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.
D. Hadfield-Menell
No textbook information available

6.4210 Robotic Manipulation
______

Undergrad (Fall)
(Subject meets with 6.4212)
Prereq: (6.100A and 6.3900) or permission of instructor
Units: 4-2-9
______
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.
R. Tedrake

6.4212 Robotic Manipulation
______

Graduate (Fall)
(Subject meets with 6.4210)
Prereq: (6.100A and 6.3900) or permission of instructor
Units: 3-0-9
______
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.
R. L. Tedrake

6.8200 Sensorimotor Learning
______

Graduate (Spring)
Prereq: 6.3900 or 6.7900
Units: 3-0-9
Lecture: TR11-12.30 (1-190)
______
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.
P. Agrawal
No textbook information available

6.8210 Underactuated Robotics
______

Graduate (Spring)
Prereq: 18.03 and 18.06
Units: 3-0-9
URL: http://underactuated.mit.edu
Lecture: TR2.30-4 (E25-111)
______
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.
R. Tedrake
No textbook information available

Graphics

6.4400 Computer Graphics
______

Undergrad (Fall)
Prereq: 6.1010 and (18.06 or 18.C06)
Units: 3-0-9
______
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.
M. Konakovic Lukovic

6.4420[J] Computational Design and Fabrication
______

Undergrad (Spring)
(Same subject as 2.0911[J])
(Subject meets with 6.8420)
Prereq: Calculus II (GIR) and (6.1010 or permission of instructor)
Units: 3-0-9
Lecture: TR2.30-4 (32-124)
______
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.
W. Matusik
No textbook information available

6.8410 Shape Analysis
______

Not offered academic year 2023-2024Graduate (Spring)
Prereq: Calculus II (GIR), 18.06, and (6.8300 or 6.4400)
Units: 3-0-9
______
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.
Staff

6.8420 Computational Design and Fabrication
______

Graduate (Spring)
(Subject meets with 2.0911[J], 6.4420[J])
Prereq: Calculus II (GIR) and (6.1010 or permission of instructor)
Units: 3-0-9
Lecture: TR2.30-4 (32-124)
______
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.
W. Matusik
No textbook information available

Human-Computer Interaction & Society

6.4510 Engineering Interactive Technologies
______

Undergrad (Fall)
Not offered regularly; consult department
Prereq: 6.1020, 6.2050, 6.2060, 6.9010, or permission of instructor
Units: 1-5-6
______
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.
Staff

6.4530[J] Principles and Practice of Assistive Technology
______

Undergrad (Fall)
Not offered regularly; consult department
(Same subject as 2.78[J], HST.420[J])
Prereq: Permission of instructor
Units: 2-4-6
______
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.
Staff

6.4550[J] Interactive Music Systems
______

Undergrad (Fall, Spring) HASS Arts
(Same subject as 21M.385[J])
(Subject meets with 21M.585)
Prereq: (6.1010 and 21M.301) or permission of instructor
Units: 3-0-9
URL: http://mta.mit.edu/music/class-schedule
Lecture: MW11-12.30 (4-270) Lab: TBA (4-270)
______
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.
Fall: E. Egozy, L. Kaelbling
Spring: E. Egozy, L. Kaelbling
No required or recommended textbooks

6.4570[J] Creating Video Games
______

Undergrad (Fall) HASS Arts
(Same subject as CMS.611[J])
Prereq: 6.100A or CMS.301
Units: 3-3-6
______
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.
P. Tan, S. Verrilli, R. Eberhardt, A. Grant

6.4590[J] Foundations of Information Policy
______

Not offered academic year 2023-2024Undergrad (Fall) HASS Social Sciences
(Same subject as STS.085[J])
(Subject meets with STS.487)
Prereq: Permission of instructor
Units: 3-0-9
______
Studies the growth of computer and communications technology and the new legal and ethical challenges that reflect tensions between individual rights and societal needs. Topics 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.
H. Abelson, M. Fischer, D. Weitzner

6.8510 Intelligent Multimodal User Interfaces
______

Graduate (Spring)
Prereq: (6.1020 and 6.4100) or permission of instructor
Units: 3-0-9
Lecture: TR11-12.30 (3-442)
______
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.
R. Davis
No required or recommended textbooks

6.8530 Interactive Data Visualization
______

Graduate (Spring)
Not offered regularly; consult department
Prereq: 6.1020
Units: 3-0-9
Credit cannot also be received for 6.C35, 6.C85, 11.154, 11.454, 11.C35, 11.C85
______
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.
A. Satyanarayan

Computational Biology

6.4710[J] Evolutionary Biology: Concepts, Models and Computation
______

Undergrad (Spring)
(Same subject as 7.33[J])
Prereq: (6.100A and 7.03) or permission of instructor
Units: 3-0-9
Lecture: TR11-12.30 (56-114)
______
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.
R. Berwick, D. Bartel
Textbooks (Spring 2024)

6.8700[J] Advanced Computational Biology: Genomes, Networks, Evolution
______

Graduate (Fall)
(Same subject as HST.507[J])
(Subject meets with 6.8701)
Prereq: (Biology (GIR), 6.1210, and 6.3700) or permission of instructor
Units: 4-0-8
______
See description for 6.047. 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.
M. Kellis

6.8701 Computational Biology: Genomes, Networks, Evolution
______

Undergrad (Fall)
(Subject meets with 6.8700[J], HST.507[J])
Prereq: (Biology (GIR), 6.1210, and 6.3700) or permission of instructor
Units: 3-0-9
______
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.
M. Kellis

6.8710[J] Computational Systems Biology: Deep Learning in the Life Sciences
______

Not offered academic year 2024-2025Graduate (Spring)
(Same subject as HST.506[J])
(Subject meets with 6.8711[J], 20.390[J], 20.490)
Prereq: Biology (GIR) and (6.3700 or 18.600)
Units: 3-0-9
Lecture: TR12.30-2 (10-250)
______
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.
B. Berger
No textbook information available

6.8711[J] Computational Systems Biology: Deep Learning in the Life Sciences
______

Not offered academic year 2024-2025Undergrad (Spring)
(Same subject as 20.390[J])
(Subject meets with 6.8710[J], 20.490, HST.506[J])
Prereq: (7.05 and (6.100B or 6.9080)) or permission of instructor
Units: 3-0-9
Lecture: TR12.30-2 (10-250)
______
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.
B. Berger
No textbook information available

6.8720[J] Principles of Synthetic Biology
______

Graduate (Fall)
(Same subject as 20.405[J])
(Subject meets with 6.8721[J], 20.305[J])
Prereq: None
Units: 3-0-9
______
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.
R. Weiss

6.8721[J] Principles of Synthetic Biology
______

Undergrad (Fall)
(Same subject as 20.305[J])
(Subject meets with 6.8720[J], 20.405[J])
Prereq: None
Units: 3-0-9
______
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.
R. Weiss

Biomedical & Health

6.4800[J] Biomedical Systems: Modeling and Inference
______

Not offered academic year 2023-2024Undergrad (Spring)
(Same subject as 22.54[J])
Prereq: (6.3100 and (18.06 or 18.C06)) or permission of instructor
Units: 4-4-4
Subject Cancelled Subject Cancelled
______
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.
E. Adalsteinsson, T. Heldt, C. M. Stultz, J. K. White

6.4810[J] Cellular Neurophysiology and Computing
______

Not offered academic year 2023-2024Undergrad (Spring)
(Same subject as 2.791[J], 9.21[J], 20.370[J])
(Subject meets with 2.794[J], 6.4812[J], 9.021[J], 20.470[J], HST.541[J])
Prereq: (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor
Units: 5-2-5
______
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.
Staff

6.4812[J] Cellular Neurophysiology and Computing
______

Not offered academic year 2023-2024Graduate (Spring)
(Same subject as 2.794[J], 9.021[J], 20.470[J], HST.541[J])
(Subject meets with 2.791[J], 6.4810[J], 9.21[J], 20.370[J])
Prereq: (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor
Units: 5-2-5
______
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.
Staff

6.4820[J] Quantitative and Clinical Physiology
______

Undergrad (Fall)
(Same subject as 2.792[J], HST.542[J])
(Subject meets with 2.796[J], 6.4822[J])
Prereq: Physics II (GIR), 18.03, or permission of instructor
Units: 4-2-6
______
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.
T. Heldt, R. G. Mark

6.4822[J] Quantitative Physiology: Organ Transport Systems
______

Graduate (Fall)
(Same subject as 2.796[J])
(Subject meets with 2.792[J], 6.4820[J], HST.542[J])
Prereq: 6.4810 and (2.006 or 6.2300)
Units: 4-2-6
______
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.
T. Heldt, R. G. Mark

6.4830[J] Fields, Forces and Flows in Biological Systems
______

Undergrad (Spring)
(Same subject as 2.793[J], 20.330[J])
Prereq: Biology (GIR), Physics II (GIR), and 18.03
Units: 4-0-8
Lecture: MWF11 (4-163) Recitation: M3 (16-220) or T10 (56-167) or W10 (56-180) or W12 (56-154) +final
______
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.
J. Han, S. Manalis
No textbook information available

6.4832[J] Fields, Forces, and Flows in Biological Systems
______

Graduate (Fall)
(Same subject as 2.795[J], 10.539[J], 20.430[J])
Prereq: Permission of instructor
Units: 3-0-9
______
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.
M. Bathe, A. Hansen

6.4840[J] Molecular, Cellular, and Tissue Biomechanics
______

Undergrad (Spring)
(Same subject as 2.797[J], 3.053[J], 20.310[J])
(Subject meets with 2.798[J], 3.971[J], 6.4842[J], 10.537[J], 20.410[J])
Prereq: Biology (GIR) and 18.03
Units: 4-0-8
Lecture: TR1-2.30 (4-237) Recitation: TBA
______
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.
P. So, R. Raman
No textbook information available

6.4842[J] Molecular, Cellular, and Tissue Biomechanics
______

Graduate (Spring)
(Same subject as 2.798[J], 3.971[J], 10.537[J], 20.410[J])
(Subject meets with 2.797[J], 3.053[J], 6.4840[J], 20.310[J])
Prereq: Biology (GIR) and 18.03
Units: 3-0-9
Lecture: TR1-2.30 (4-237)
______
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.
P. So, R. Raman
No textbook information available

6.4860[J] Medical Device Design
______

Undergrad (Spring)
(Same subject as 2.750[J])
(Subject meets with 2.75[J], 6.4861[J], HST.552[J])
Prereq: 2.008, 6.2040, 6.2050, 6.2060, 22.071, or permission of instructor
Units: 3-3-6
URL: https://meddevdesign.mit.edu/
Lecture: MW1-2.30 (3-270)
______
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.
A. H. Slocum, E. Roche, N. C. Hanumara, G. Traverso, A. Pennes
No textbook information available

6.4861[J] Medical Device Design
______

Graduate (Spring)
(Same subject as 2.75[J], HST.552[J])
(Subject meets with 2.750[J], 6.4860[J])
Prereq: 2.008, 6.2040, 6.2050, 6.2060, 22.071, or permission of instructor
Units: 3-3-6
URL: https://meddevdesign.mit.edu/
Lecture: MW1-2.30 (3-270)
______
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.
A. H. Slocum, E. Roche, N. C. Hanumara, G. Traverso, A. Pennes
No textbook information available

6.4880[J] Biological Circuit Engineering Laboratory
______

Undergrad (Spring) Institute Lab
(Same subject as 20.129[J])
Prereq: Biology (GIR) and Calculus II (GIR)
Units: 2-8-2
Lecture: MW12 (4-163) Lab: MW1-5 (26-035)
______
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.
J. Niles, R. Weiss, J. Buck
No required or recommended textbooks

6.4900 Introduction to EECS via Medical Technology
______

Undergrad (Spring) Institute Lab
Not offered regularly; consult department
Prereq: Calculus II (GIR) and Physics II (GIR)
Units: 4-4-4
______
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.
C. M. Stultz, E. Adalsteinsson

6.8800[J] Biomedical Signal and Image Processing
______

Graduate (Spring)
(Same subject as 16.456[J], HST.582[J])
(Subject meets with 6.8801[J], HST.482[J])
Prereq: (6.3700 and (2.004, 6.3000, 16.002, or 18.085)) or permission of instructor
Units: 3-1-8
Lecture: TR9.30-11 (34-304) Lab: F9 (34-301) or F10 (34-301)
______
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.
M. Alam
No textbook information available

6.8801[J] Biomedical Signal and Image Processing
______

Undergrad (Spring)
(Same subject as HST.482[J])
(Subject meets with 6.8800[J], 16.456[J], HST.582[J])
Prereq: (6.3700 or permission of instructor) and (2.004, 6.3000, 16.002, or 18.085)
Units: 3-1-8
Lecture: TR9.30-11 (34-304) Lab: F9 (34-301) or F10 (34-301)
______
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.
M. Alam
No textbook information available

6.8810[J] Data Acquisition and Image Reconstruction in MRI
______

Not offered academic year 2024-2025Graduate (Fall)
(Same subject as HST.580[J])
Prereq: 6.3010
Units: 3-0-9
______
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.
E. Adalsteinsson

6.8830[J] Signal Processing by the Auditory System: Perception
______

Graduate (Fall)
Not offered regularly; consult department
(Same subject as HST.716[J])
Prereq: (6.3000 and (6.3700 or 6.3702)) or permission of instructor
Units: 3-0-9
______
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.
Staff

Vision

6.8300 Advances in Computer Vision
______

Graduate (Spring)
(Subject meets with 6.8301)
Prereq: (6.1200 or 6.3700) and (18.06 or 18.C06)
Units: 3-0-9
URL: https://advances-in-vision.github.io/
Lecture: TR1-2.30 (26-100)
______
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.
S. Beery, M. Konakovic Lukovic, V. Sitzmann
No textbook information available

6.8301 Advances in Computer Vision
______

Undergrad (Spring)
(Subject meets with 6.8300)
Prereq: (6.1200 or 6.3700) and (18.06 or 18.C06)
Units: 4-0-11
URL: https://advances-in-vision.github.io/
Lecture: TR1-2.30 (26-100)
______
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.
S. Beery, M. Konakovic Lukovic, V. Sitzmann
No textbook information available

6.8320 Advanced Topics in Computer Vision
______

Graduate (Fall) Can be repeated for credit
Not offered regularly; consult department
Prereq: 6.801, 6.8300, or permission of instructor
Units: 3-0-9
______
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.
W. T. Freeman, B. K. P. Horn, A. Torralba

6.8370 Advanced Computational Photography
______

Not offered academic year 2023-2024Graduate (Fall)
(Subject meets with 6.8371)
Prereq: Calculus II (GIR) and 6.1020
Units: 3-0-9
______
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.
F. P. Durand

6.8371 Digital and Computational Photography
______

Not offered academic year 2023-2024Undergrad (Fall)
(Subject meets with 6.8370)
Prereq: Calculus II (GIR) and 6.1010
Units: 3-0-9
______
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.
F. P. Durand

Natural Language Processing & Speech

6.8610 Quantitative Methods for Natural Language Processing
______

Graduate (Fall)
(Subject meets with 6.8611)
Prereq: 6.3900 and (18.06 or 18.C06)
Units: 3-0-9
______
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.
Y. Kim

6.8611 Quantitative Methods for Natural Language Processing
______

Undergrad (Fall)
(Subject meets with 6.8610)
Prereq: 6.3900 and (18.06 or 18.C06)
Units: 4-0-11
______
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.
Y. Kim

6.8620[J] Spoken Language Processing
______

Not offered academic year 2024-2025Graduate (Spring)
(Same subject as HST.728[J])
Prereq: 6.3000 and 6.3900
Units: 3-1-8
Lecture: TR9.30-11 (32-124)
______
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.
J. R. Glass
No required or recommended textbooks

6.8630[J] Natural Language and the Computer Representation of Knowledge
______

Not offered academic year 2023-2024Graduate (Spring)
(Same subject as 9.611[J], 24.984[J])
Prereq: 6.4100
Units: 3-3-6
______
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.
Staff


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