Course 6: Electrical Engineering and Computer Science
IAP/Spring 2025

Course 6 Home CI-M Subjects for Undergraduate Majors IAP only Evaluations (Certificates Required)

6.10/6.50

6.20/6.60

6.30/6.70

6.40/6.80

6.90/6.ZZ

Cross-cutting EECS Subjects

6.9000 Engineering for Impact

(

)
Prereq: 6.1910, 6.2000, and 6.3100
Units: 2-3-7

Lecture: TR11-12.30 (34-304) Lab: TBA
______

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.
J. Voldman
No textbook information available

6.9010 Introduction to EECS via Interconnected Embedded Systems

(

)

Not offered regularly; consult department
Prereq: 6.100A; Coreq: Physics II (GIR)
Units: 1-5-6
______

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.
Staff

6.9020[J] How to Make (Almost) Anything

(

)
(Same subject as 4.140[J], MAS.863[J])
Prereq: Permission of instructor
Units: 3-9-6
______

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.
N. Gershenfeld

6.9030 Strobe Project Laboratory

(

)

Prereq: Physics II (GIR) or permission of instructor
Units: 2-8-2

Lecture: MW12 (4-257) Lab: TBA
______

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.
Fall: J. Bales
Spring: J. Bales
No required or recommended textbooks

6.9080 Introduction to EECS via Robotics

(

)

Not offered regularly; consult department
Prereq: 6.100A or permission of instructor
Units: 2-4-6
______

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.
Staff

6.UAR Seminar in Undergraduate Advanced Research

(

)

Prereq: Permission of instructor
Units: 2-0-4

Lecture: R4 (32-123)
______

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.
Fall: D. Katabi
Spring: D. Katabi
No textbook information available

6.UAT Oral Communication

(

)
Prereq: None
Units: 3-0-6

Lecture: MW10 (32-155)
______

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.
Fall: T. L. Eng
Spring: T. L. Eng
No textbook information available

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.
Fall: B. Kotelly
Spring: B. Kotelly
No textbook information available

6.9110 Engineering Leadership Lab

(

)

Engineering School-Wide Elective Subject.
(Offered under: 6.9110, 16.650)
(Subject meets with 6.9130[J], 16.667[J])
Prereq: None. Coreq: 6.9120; or permission of instructor
Units: 0-2-1

Lab: F9-11 (32-144) or F1-3 (32-144) or F3-5 (32-144)
______

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.
Fall: L. McGonagle, J. Feiler
Spring: L. Mcgonagle
No required or recommended textbooks

6.9120 Engineering Leadership

(

)

Engineering School-Wide Elective Subject.
(Offered under: 6.9120, 16.651)
Prereq: None. Coreq: 6.9110; or permission of instructor
Units: 1-0-2

Lecture: M11-12.30 (4-153) or M1-2.30 (4-153) or T11-12.30 (4-153) or T1-2.30 (4-153)
______

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.
Fall: J. Magarian, E. Schanne
Spring: J. Magarian, E. Schanne
No required or recommended textbooks

6.9130 Engineering Leadership Lab

(

)

Engineering School-Wide Elective Subject.
(Offered under: 6.9130, 16.667)
(Subject meets with 6.9110[J], 16.650[J])
Prereq: 6.910A, 6.9110, 6.9120, or permission of instructor
Units: 0-2-4

Lab: F9-11 (32-144) or F1-3 (32-144) or F3-5 (32-144)
______

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.
Fall: L. Mcgonagle
Spring: L. Mcgonagle
No required or recommended textbooks

6.9140 Project Engineering

(

); first half of term
Engineering School-Wide Elective Subject.
(Offered under: 6.9140, 16.669)
Prereq: (6.910A and (6.9110 or 6.9120)) or permission of instructor
Units: 4-0-0 [P/D/F]
IAP:

Begins Jan 27. Lab: RF9-5 (CAMP CODY)
Spring:

Ends Mar 21. Lecture: W3-5 (32-144)
______

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.
IAP: O. de Weck, J. Feiler, L. McGonagle, R. Rahaman
Spring: J. Feiler
No textbook information available

(

)
Prereq: None
Units: 2-0-4
______

Focuses around the premise that the abilities to negotiate with, and influence others, are essential to being an effective leader in technology rich environments. Provides graduate students with underlying principles and a repertoire of negotiation and influence skills that apply to interpersonal situations, particularly those where an engineer or project leader lacks formal authority over others in delivering results. Utilizes research-based approaches through the application of multiple learning methods, including experiential role plays, case studies, assessments, feedback, and personal reflections. Concepts such as the zone of possible agreements, best alternative to negotiated agreements, and sources of influence are put into practice. Satisfies the requirements for the Graduate Certificate in Technical Leadership.
R. Best

6.9700 Studies in Artificial Intelligence and Decision Making

(

)
Not offered regularly; consult department
Prereq: Permission of department
Units: 0-0-48 [P/D/F]
______

Introduction to artificial intelligence and decision making in a series of online subjects followed by a comprehensive examination. Probability: distributions and probabilistic calculations, inference methods, laws of large numbers, and random processes. Statistical data analysis: linear regression, parameter estimation, hypothesis testing, model selection, and causal inference. Machine learning: linear classification, fundamentals of supervised machine learning, deep learning, unsupervised learning, and generative models. Online decision making: online optimization, online learning, Markov decision processes and reinforcement learning, elements of control theory, and fundamentals of game theory. Computer vision: fundamentals of image and signal processing, introduction to machine learning for vision, generative models and representation learning, and elements of scene understanding. Restricted to Artificial Intelligence and Decision Making MicroMasters Credential holders in the AI+D Blended Master's program.
A. Madry, P. Parrilo

6.9710 Internship in Artificial Intelligence and Decision Making

(

)
Prereq: Permission of department
Units arranged [P/D/F]
______

)
Prereq: None
Units: 0-1-0 [P/D/F]
IAP:

TBA.
Spring:

TBA.

Required for Course 6 MEng students to gain professional experience in electrical engineering or computer science through an internship (industry, government, or academic) of 4 or more weeks in IAP or summer. This can be completed as MEng students or as undergrads, through previous employment completed while deferring MEng entry or by attending a series of three colloquia, seminars, or technical talks related to their field. For internships/work experience, a letter from the employer confirming dates of employment is required. All students are required to write responses to short essay prompts about their professional experience. International students must consult ISO and the EECS Undergraduate Office on work authorization and allowable employment dates.
Fall: K. Lacurts
IAP: K. Lacurts
Spring: K. Lacurts
No required or recommended textbooks

6.9840 Practical Experience in EECS

(

)

Prereq: None
Units: 0-1-0 [P/D/F]
IAP:

TBA.
Spring:

TBA.

For Course 6 students in the MEng program who seek practical off-campus research experiences or internships in electrical engineering or computer science. Before enrolling, students must have an offer of employment from a company or organization and secure an advisor within EECS. Employers must document the work accomplished. Proposals subject to departmental approval. For students who begin the MEng program in the summer only, the experience or internship cannot exceed 20 hours per week and must begin no earlier than the first day of the Summer Session, but may end as late as the last business day before the Fall Term.
Fall: M. Bittrich
IAP: M. Bittrich
Spring: M. Bittrich
No textbook information available

6.9850 6-A Internship

(

)
Prereq: None
Units: 0-12-0 [P/D/F]

TBA.

Provides academic credit for the first assignment of 6-A undergraduate students at companies affiliated with the department's 6-A internship program. Limited to students participating in the 6-A internship program.
Fall: P. Capistrano
Spring: P. Capistrano
No textbook information available

6.9860 Advanced 6-A Internship

(

)
Prereq: 6.9850
Units: 0-12-0 [P/D/F]

TBA.

Provides academic credit for the second assignment of 6-A undergraduate students at companies affiliated with the department's 6-A internship program. Limited to students participating in the 6-A internship program.
Fall: P. Capistrano
Spring: P. Capistrano
No textbook information available

6.9870 Graduate 6-A Internship

(

)
Prereq: 6.9850 or 6.9860
Units: 0-12-0 [P/D/F]

TBA.

Provides academic credit for a graduate assignment of graduate 6-A students at companies affiliated with the department's 6-A internship program. Limited to graduate students participating in the 6-A internship program.
Fall: P. Capistrano
Spring: P. Capistrano
No textbook information available

(

)
Prereq: None
Units arranged [P/D/F]
______

(

)

Prereq: Permission of instructor
Units arranged [P/D/F]

TBA.

)

)

Prereq: None
Units: 3-0-9
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
H. Balakrishnan

)

Not offered regularly; consult department
Prereq: None
Units: 3-0-9
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S057 Special Subject in Electrical Engineering and Computer Science

(

)

Prereq: None
Units arranged
URL: https://www.eecs.mit.edu/academics/subject-updates/

TBA.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
A. Chlipala
No textbook information available

6.S058 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: None
Units arranged
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

(

)

; first half of term
Prereq: Permission of instructor
Units arranged
URL: https://www.eecs.mit.edu/academics/subject-updates/special-subjects-spring-2025/

Ends Mar 21. Lecture: W1-4 (4-257)
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
T. Heldt
No textbook information available

6.S078 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Staff

6.S079 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Staff

6.S080 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S081 Special Subject in Electrical Engineering and Computer Science

(

)

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S088 Special Subject in Electrical Engineering and Computer Science

(

)

Prereq: Permission of instructor
Units arranged [P/D/F]
URL: IAP URL: https://www.eecs.mit.edu/academics/iap-offerings/iap-2025/

Ends Jan 17. Lecture: MTWRF4 (4-163)
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
D. Karger
No textbook information available

6.S089 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Staff

6.S188 Special Laboratory Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Staff

6.S189 Special Laboratory Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Staff

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S197 Special Laboratory Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S193-6.S198 Special Laboratory Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged
______

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
Consult Department

6.S630 Special Subject in Engineering Leadership

(

)

Prereq: None
Units arranged
URL: https://https://gelp.mit.edu/grad-people-products-projects

Lecture: TR10-12 (26-322)
______

Covers subject matter not offered in the regular curriculum. Consult the Gordon Engineering Leadership Program or Riccio Graduate Engineering Leadership Program to learn of offerings for a particular term.
A. Hu
No textbook information available

6.S640 Special Subject in Engineering Leadership

(

)

Prereq: None
Units arranged
URL: https://gelp.mit.edu/grad-research-matter

Lecture: T EVE (5-7 PM) (32-082)
______

6.S650 Special Subject in Engineering Leadership

(

)

Prereq: None
Units arranged
______

6.S660 Special Subject in Engineering Leadership

(

)

Prereq: None
Units arranged

Lecture: W1,F10-12 (26-328)
______

Covers subject matter not offered in the regular curriculum.
S. Leeb
No textbook information available

6.S913 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Covers subject matter not offered in the regular curriculum.
Staff

6.S914 Special Subject in Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Covers subject matter not offered in the regular curriculum.
Staff

6.S915 Special Subject in Electrical Engineering and Computer Science

(

)

Prereq: Permission of instructor
Units arranged [P/D/F]
URL: IAP URL: https://www.eecs.mit.edu/academics/iap-offerings/iap-2024/

Meets 1/13 to 1/17. Lecture: TWF1-3 (24-115)
______

Covers subject matter not offered in the regular curriculum.
C. Uhler
No textbook information available

(

)

Not offered regularly; consult department
Prereq: Permission of instructor
Units arranged [P/D/F]
______

Covers subject matter not offered in the regular curriculum.
Staff

6.S950 Special Subject in Electrical Engineering and Computer Science

(

)

; first half of term
Prereq: None
Units arranged
URL: https://www.eecs.mit.edu/academics/subject-updates/special-subjects-spring-2025/

Ends Mar 14. Lecture: W4-5.30 (E62-223)
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
W. Oliver
No textbook information available

6.S951 Special Subject in Electrical Engineering and Computer Science

(

)

Prereq: None
Units: 3-0-9
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
S. Bates

(

)

Not offered regularly; consult department
Prereq: None
Units arranged
______

6.S962 Special Studies: Electrical Engineering and Computer Science

(

)

Not offered regularly; consult department
Prereq: None
Units arranged
______

6.S963-6.S967 Special Studies: EECS

(

)

Prereq: None
Units arranged
URL: 6.S963: https://www.eecs.mit.edu/academics/subject-updates/special-subjects-spring-2025/
6.S966: https://www.eecs.mit.edu/academics/subject-updates/special-subjects-spring-2025/

6.S963: Begins Mar 31. Lecture: MW2.30-4 (36-144)

6.S966: Lecture: MW2.30-4 (4-149)
______

6.S974 Special Subject in Electrical Engineering and Computer Science

(

)

Prereq: None
Units arranged
______

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
V. Vassilevska Williams

6.S975 Special Subject in Electrical Engineering and Computer Science

(

(

); second half of term
(Subject meets with 6.C511)
Prereq: 6.100A, 6.C01, (6.1200 or 6.3700), and (18.06 or 18.C06)
Units: 3-0-3

Begins Mar 31. Lecture: MW2.30-4 (32-144)
______

Focuses on in-depth modeling of engineering tasks as machine learning problems. Emphasizes framing, method design, and interpretation of results. In comparison to broader prerequisite 6.C01, this project-oriented subject consists of deep dives into select technical areas or engineering tasks involving both supervised and exploratory uses of machine learning. Explores technical areas such robustness, interpretability, fairness and engineering tasks such as recommender systems, performance optimization, and automated design. Students taking graduate version complete additional assignments. Students cannot receive credit without completion of the core subject 6.C01. Enrollment may be limited.
S. Shen
No textbook information available

6.C06[J] Linear Algebra and Optimization

(

)

(Same subject as 18.C06[J])
Prereq: Calculus II (GIR)
Units: 5-0-7
Credit cannot also be received for 18.06, 18.700, ES.1806
______

Introductory course in linear algebra and optimization, assuming no prior exposure to linear algebra and starting from the basics, including vectors, matrices, eigenvalues, singular values, and least squares. Covers the basics in optimization including convex optimization, linear/quadratic programming, gradient descent, and regularization, building on insights from linear algebra. Explores a variety of applications in science and engineering, where the tools developed give powerful ways to understand complex systems and also extract structure from data.
S. Johnson

6.C25[J] Real World Computation with Julia

(

)
(Same subject as 1.C25[J], 12.C25[J], 16.C25[J], 18.C25[J], 22.C25[J])
Prereq: 6.100A, 18.03, and 18.06
Units: 3-0-9
______

Focuses on algorithms and techniques for writing and using modern technical software in a job, lab, or research group environment that may consist of interdisciplinary teams, where performance may be critical, and where the software needs to be flexible and adaptable. Topics include automatic differentiation, matrix calculus, scientific machine learning, parallel and GPU computing, and performance optimization with introductory applications to climate science, economics, agent-based modeling, and other areas. Labs and projects focus on performant, readable, composable algorithms, and software. Programming will be in Julia. Expects students to have some familiarity with Python, Matlab, or R. No Julia experience necessary.
A. Edelman, R. Ferrari, B. Forget, C. Leiseron,Y. Marzouk, J. Williams

6.C27[J] Computational Imaging: Physics and Algorithms

(

)
(Same subject as 2.C27[J], 3.C27[J])
(Subject meets with 2.C67[J], 3.C67[J], 6.C67[J])
Prereq: 18.C06 and (1.00, 1.000, 2.086, 3.019, or 6.100A)
Units: 3-0-9
______

Explores the contemporary computational understanding of imaging: encoding information about a physical object onto a form of radiation, transferring the radiation through an imaging system, converting it to a digital signal, and computationally decoding and presenting the information to the user. Introduces a unified formulation of computational imaging systems as a three-round "learning spiral": the first two rounds describe the physical and algorithmic parts in two exemplary imaging systems. The third round involves a class project on an imaging system chosen by students. Undergraduate and graduate versions share lectures but have different recitations. Involves optional "clinics" to even out background knowledge of linear algebra, optimization, and computational imaging-related programming best practices for students of diverse disciplinary backgrounds. Students taking graduate version complete additional assignments.
G. Barbastathis

6.C35[J] Interactive Data Visualization and Society

(

)
(Same subject as 11.C35[J], CMS.C35[J], IDS.C35[J])
(Subject meets with 6.C85[J], 11.C85[J], IDS.C85[J])
Prereq: None
Units: 3-4-8
Credit cannot also be received for 6.8530, 11.154, 11.454

Lecture: MW1-2.30 (45-230) Lab: R3 (45-230)
______

Covers the design, ethical, and technical skills for creating effective visualizations. Short assignments build familiarity with the data analysis and visualization design process. Weekly lab sessions present coding and technical skills. A final project provides experience working with real-world big data, provided by external partners, in order to expose and communicate insights about societal issues. Students taking graduate version complete additional assignments. Enrollment limited. Enrollment limited.
A. Satyanarayan
No textbook information available

6.C40[J] Ethics of Computing
(New)

(

)

(Same subject as 24.C40[J])
Prereq: None
Units: 3-0-9
______

Explores ethical questions raised by the potentially transformative power of computing technologies. Topics include: lessons from the history of transformative technologies; the status of property and privacy rights in the digital realm; effective accelerationism, AI alignment, and existential risk; algorithmic bias and algorithmic fairness; and free speech, disinformation, and polarization on online platforms.
B. Skow, A. Solar-Lezama

6.C51 Modeling with Machine Learning: from Algorithms to Applications

(

); first half of term
(Subject meets with 6.C01)
Prereq: Calculus II (GIR) and 6.100A; Coreq: 1.C51, 2.C51, 3.C51, 6.C511, 7.C51, 22.C51, or SCM.C51
Units: 2-0-4

Ends Mar 21. Lecture: MW2.30-4 (32-123)
______

6.C511 Modeling with Machine Learning for Computer Science
(New)

(

); second half of term
(Subject meets with 6.C011)
Prereq: 6.100A, 6.C51, (6.1200 or 6.3700), and (18.06 or 18.C06)
Units: 3-0-3

Begins Mar 31. Lecture: MW2.30-4 (32-144)
______

Focuses on in-depth modeling of engineering tasks as machine learning problems. Emphasizes framing, method design, and interpretation of results. In comparison to broader co-requisite 6.C01/6.C51, this project oriented subject consists of deep dives into select technical areas or engineering tasks involving both supervised and exploratory uses of machine learning. Deep dives into technical areas such robustness, interpretability, fairness; engineering tasks such as recommender systems, performance optimization, automated design. Students taking graduate version complete additional assignments. Students cannot receive credit without completion of the core subject 6.C51. Enrollment may be limited.
S. Shen
No textbook information available

6.C57[J] Optimization Methods
(New)

(

)
(Same subject as 15.C57[J], IDS.C57[J])
(Subject meets with 6.C571[J], 15.C571[J])
Prereq: 18.C06 or permission of instructor
Units: 4-0-8
______

Introduction to the methods and applications of optimization. Topics include linear optimization, duality, non-linear optimization, integer optimization, and optimization under uncertainty. Instruction provided in modeling techniques to address problems arising in practice, mathematical theory to understand the structure of optimization problems, computational algorithms to solve complex optimization problems, and practical applications. Covers several examples and in-depth case studies based on real-world data to showcase impactful applications of optimization across management and engineering. Computational exercises based on the Julia-based programming language JuMP. Includes a term project. Basic competency in computational programming and linear algebra recommended. Students taking graduate version complete additional assignments. This subject was previously listed as 15.093/6.7200/IDS.200.
A. Jacquillat, H. Lu

6.C571[J] Optimization Methods
(New)

(

)
(Same subject as 15.C571[J])
(Subject meets with 6.C57[J], 15.C57[J], IDS.C57[J])
Prereq: 18.C06 or permission of instructor
Units: 4-0-8
______

6.C67[J] Computational Imaging: Physics and Algorithms

(

)
(Same subject as 2.C67[J], 3.C67[J])
(Subject meets with 2.C27[J], 3.C27[J], 6.C27[J])
Prereq: 18.C06 and (1.00, 1.000, 2.086, 3.019, or 6.100A)
Units: 3-0-9
______

Contemporary understanding of imaging is computational: encoding onto a form of radiation the information about a physical object, transferring the radiation through the imaging system, converting it to a digital signal, and computationally decoding and presenting the information to the user. This class introduces a unified formulation of computational imaging systems as a three-round "learning spiral": the first two rounds, instructors describe the physical and algorithmic parts in two exemplary imaging systems. The third round, students conduct themselves as the class project on an imaging system of their choice. The undergraduate and graduate versions share lectures but have different recitations. Throughout the term, we also conduct optional "clinics" to even out background knowledge of linear algebra, optimization, and computational imaging-related programming best practices for students of diverse disciplinary backgrounds.
G. Barbastathis

6.C85[J] Interactive Data Visualization and Society

(

)
(Same subject as 11.C85[J], IDS.C85[J])
(Subject meets with 6.C35[J], 11.C35[J], CMS.C35[J], IDS.C35[J])
Prereq: None
Units: 3-1-8
Credit cannot also be received for 6.8530, 11.154, 11.454

Lecture: MW1-2.30 (45-230) Lab: R4 (45-230)
______

Covers the design, ethical, and technical skills for creating effective visualizations. Short assignments build familiarity with the data analysis and visualization design process. Students participate in hour-long studio reading sessions. A final project provides experience working with real-world big data, provided by external partners, in order to expose and communicate insights about societal issues. Students taking graduate version complete additional assignments.
A. Satyanarayan
No textbook information available

6.10/6.50

6.20/6.60

6.30/6.70

6.40/6.80

6.90/6.ZZ

Course 6: Electrical Engineering and Computer ScienceIAP/Spring 2025

Cross-cutting EECS Subjects

6.9000 Engineering for Impact

6.9010 Introduction to EECS via Interconnected Embedded Systems

6.9020[J] How to Make (Almost) Anything

6.9030 Strobe Project Laboratory

6.9080 Introduction to EECS via Robotics

6.UAR Seminar in Undergraduate Advanced Research

6.UAT Oral Communication

Gordon Engineering Leadership Program

6.9101[J] Introduction to Design Thinking and Innovation in Engineering

6.910A Design Thinking and Innovation Leadership for Engineers

6.910B Design Thinking and Innovation Project

6.9110 Engineering Leadership Lab

6.9120 Engineering Leadership

6.9130 Engineering Leadership Lab

6.9140 Project Engineering

6.9150[J] Leading Innovation in Teams

6.9160[J] Engineering Innovation: Global Security Systems

6.9162[J] Engineering Innovation: Global Security Systems (New)

6.9250 Leadership: People, Products, Projects (New)

6.9260 Multistakeholder Negotiation for Technical Experts

6.9270 Negotiation and Influence Skills for Technical Leaders

6.9280[J] Leading Creative Teams

6.EPE UPOP Engineering Practice Experience

6.EPW UPOP Engineering Practice Workshop

EECS & Beyond

6.9302[J] StartMIT: Exploring Entrepreneurship and Innovation

6.9310 Patents, Copyrights, and the Law of Intellectual Property

6.9320 Ethics for Engineers

6.9321 Ethics for Engineers - Independent Inquiry

6.9350[J] Financial Market Dynamics and Human Behavior

6.9360 Management in Engineering

Independent Activities Period

6.9500 Introduction to MATLAB

6.9510 Introduction to Signals and Systems, and Feedback Control

6.9520 Introduction to Electrical Engineering Lab Skills

6.9550 Structure and Interpretation of Computer Programs

6.9560 Introduction to Software Engineering in Java

6.9570 Introduction to C and C++

6.9600 Mobile Autonomous Systems Laboratory: MASLAB

6.9610 The Battlecode Programming Competition

6.9620 Web Lab: A Web Programming Class and Competition

6.9630 Pokerbots Competition

Non-classroom & Career

6.9700 Studies in Artificial Intelligence and Decision Making

6.9710 Internship in Artificial Intelligence and Decision Making

6.9720 Research in Artificial Intelligence and Decision Making

6.9800 Independent Study in Electrical Engineering and Computer Science

6.9820 Practical Internship Experience

6.9830 Professional Perspective Internship

6.9840 Practical Experience in EECS

6.9850 6-A Internship

6.9860 Advanced 6-A Internship

6.9870 Graduate 6-A Internship

6.9880 Graduate 6-A Internship

6.9900 Teaching Electrical Engineering and Computer Science

6.9910 Research in Electrical Engineering and Computer Science

6.9920 Introductory Research in Electrical Engineering and Computer Science

6.9930 Networking Seminars in EECS

6.9932 Introduction to Research in Electrical Engineering and Computer Science

6.9940 Professional Perspective I

6.9950 Professional Perspective II

6.9960 Experience in Technical Communication

6.9970 Academic Job Search

6.9990 Independent Study in Electrical Engineering and Computer Science

6.THG Graduate Thesis

6.THM Master of Engineering Program Thesis

6.UR Undergraduate Research in Electrical Engineering and Computer Science

Special Subjects

6.S040 Special Subject in Electrical Engineering and Computer Science

6.S041 Special Subject in Electrical Engineering and Computer Science

6.S042 Special Subject in Electrical Engineering and Computer Science

6.S043 Special Subject in Electrical Engineering and Computer Science

6.S044 Special Subject in Electrical Engineering and Computer Science

6.S045 Special Subject in Electrical Engineering and Computer Science

6.S046 Special Subject in Electrical Engineering and Computer Science

6.S047 Special Subject in Electrical Engineering and Computer Science

6.S050 Special Subject in Electrical Engineering and Computer Science

6.S051 Special Subject in Electrical Engineering and Computer Science

Course 6: Electrical Engineering and Computer Science
IAP/Spring 2025

6.9162[J] Engineering Innovation: Global Security Systems
(New)

6.9250 Leadership: People, Products, Projects
(New)