6.S041 Algorithmic Solutions to Human Problems: Integrating Artificial Intelligence and Behavioral Science
- Level: U
- Units: 3-0-9
- Prereqs: (6.3700 or 6.3800 or 18.05 or 18.600 or 14.300 or 14.32) and (6.3900 or 6.C01)
- Schedule: TR1-2.30, room E51-057
- Instructor: Sendhil Mullainathan (mullain@mit.edu), Visiting Professor in EECS and Economics; and Prof. Ashesh Rambachanv (asheshr@mit.edu)
- Satisfies: II; 6-4 Human-centric track, 6-3 Computers & Society track; 6-14 CS Restricted elective
Description
There are many challenging human problems for which machine learning algorithms could provide a solution. One specific example: Many poor people do not get benefits they are eligible for (e.g. food stamps) because the application process is psychologically overwhelming. If we can build a copilot for coding, can we build a red tape copilot that helps people navigate this process? Building such AI solutions, however, requires more than coding skills and data. It requires a more sophisticated model of machine learning (novel computational frameworks), a deeper understanding of people (insights from behavioral economics), and computational models that integrate the two. This class teaches those three components. Additionally, it will introduce students to new consequential problems where thoughtful AI applications can have outsized impact, such as unemployed workers searching for jobs, doctors making diagnostic errors, and the poor in developing countries making farming decisions. It is designed for students who are either (i) interested in learning more about the research at the frontier of behavioral economics and machine learning; or (ii) eager to put computer science to meaningful use in the world.
6.S046/6.S976 Silicon Photonics
- Level: U/G
- Units: 3-0-9
- Prereqs: 6.2300 or 8.07
- Schedule: MW1-2:30, room 26-328
- Instructor: Prof. Jelena Notaros (notaros@mit.edu)
- Satisfies: Physics Concentration, AUS2, DLAB, AAGS, grad_AUS2, EE Electromagnetics and Photonics Track, TQE(6.S976)
Description
Covers the foundational concepts behind silicon photonics based in electromagnetics, optics, and device physics; the design of silicon-photonics-based devices (including waveguides, couplers, splitters, resonators, antennas, modulators, detectors, and lasers) using both theoretical analysis and numerical simulation tools; the engineering of silicon-photonics-based circuits and systems with a focus on a variety of applications areas (spanning computing, communications, sensing, quantum, displays, and biophotonics); the development of silicon-photonics-based platforms, including fabrication and materials considerations; and the characterization of these silicon-photonics-based devices and systems through hands-on laboratory demonstrations and projects. Students taking graduate version complete additional assignments.
6.S052/6.S952 Modeling with Machine Learning for Computer Science
- Level: U/G
- Units: 3-0-3
- Prereqs:6.S084/18.061 or 18.06 or equivalent; 6.1200 or 6.3700, 6.100A or equivalent; Co-req 6.C01/6.C51
- Schedule: F11, room 56-154
- Instructor: Prof. Tommi Jaakkola (tommi@csail.mit.edu)
- Satisfies: Combination of 6.C01 +6.S052: AUS, II, 6-4 Data-centric, 6-2 Systems Science Track; old 6-2 and old 6-3 AI header; combination 6.C51+6.S952 AAGS, grad_AUS; Concentration subject in AI
Description
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, privacy or causal discovery;
engineering tasks such as recommender systems, performance optimization, or automated
design. This 6-unit subject must be taken together with the 6-unit core subject 6.C01/6.C51.
Enrollment may be limited.
6.S059 Causal Inference (meets with 15.C08, 17.C08)
- Level: U
- Units: 4-0-8
- Prereqs: 6.3800, 6.3900, 6.C01, 14.32, 17.803, 18.05, 18.650, or permission of instructor
- Schedule: MW3-4:30, room 66-168
- Instructor: Profs. Joseph Doyle (jjdoyle@mit.edu), Teppei Yamamoto (teppei@mit.edu), Roberto Rigobon (rigoban@mit.edu)
- Satisfies: AUS; 6-4 Decision-centric track; EE Track in System Science
Description
Provides an accessible overview of modern quantitative methods for causal inference: testing whether an action causes an outcome to occur. Makes heavy use of applied, real-data examples using Python or R and drawn from the participating domains (economics, political science, business, public policy, etc.). Covers topics including potential outcomes, causal graphs, randomized controlled trials, observational studies, instrumental variable estimation, and a contrast with machine learning techniques. Seeks to provide an intuitive understanding of the core concepts and techniques to help students produce and consume evidence of causal claims.
6.S077 (U)/6.S897 (G) Life Science & Semiconductor (first half term)
- Level: U/G
- Units: 3-0-3
- Prereqs: 6.2000
- Schedule: W1-4, room 4-257, ends March 22
- Instructor: Professor Thomas Heldt (thomas@mit.edu) and Ahmad Bahai (abahai@mit.edu)
- Satisfies:
Description
In this course we review the important role of semiconductor devices in patient monitoring and point of care. This includes technologies such as electrochemical, ultrasonic,magnetic, optical, and RF sensing modalities. We cover some of the basics of each device as well as physics and biology of device/human interaction.
More information and QR code can be found here.
6.S079 Software Systems for Data Science
- Level: U
- Units: 3-0-9
- Prereqs: 6.100A and 6.100B and 6.1210, or permission of instructor
- Schedule: TR2:30-4, room 32-155
- Instructor: Prof. Sam Madden (madden@csail.mit.edu)
- Satisfies: AUS; 6-4 AUS, II, 6-3 Track in Systems
Description
This class will survey techniques and systems for ingesting, efficiently processing, analyzing, and visualizing large data sets. Topics will include data cleaning, data integration, scalable systems (relational databases, NoSQL, Spark, etc.), analytics (data cubes, scalable statistics and machine learning), fundamental statistics and machine learning and scalable visualization of large data sets. The goal of the class is to gain working experience along with in-depth discussions of the topics covered. Students should have a background in programming and algorithms. There will be a semester-long project and paper, and hands-on labs designed to give experience with state of the art data processing tools.
Classes consist of lectures and readings related to course topics. Grades in 6. are assigned based on a semester long project, and about 6-8 labs of varying length, and two exams.
6.S630 Leadership – People, Products, Projects
- Level: G
- Units: 4-0-5
- Prereqs:
- Instructor: Tony Hu (tonyhu@mit.edu) Director, Graduate Engineering Leadership Program
- Schedule: TR10-12, room 26-322
Description
Apply leadership principles to a team-based product development project over the course of the semester. Identify worthy problems to tackle, generate creative concepts, make quick prototypes, and test them with stakeholders. Use product management tools to identify user needs, translate needs into design elements, and develop product roadmaps. Use project management tools to mobilize your team and organize your deliverables. Practice effective teamwork, persuasive presentations, and influencing strategies.
The project will be centered around a broad theme with the opportunity to design the product, service, or user interface.
Each class will include discussion around a new topic relating to the project or leadership skills, experiential learning around the topic, and time for team meetings.The teaching team and mentors will be available for team meetings.
This course counts toward the Graduate Certificate in Technical Leadership.
More information can be found here.
6.S640 How will my research matter? Optimizing projects towards impact
- Level: G
- Units: 2-0-4
- Prereqs:
- Instructor: Tony Hu (tonyhu@mit.edu), Director, Graduate Engineering Leadership Program
- Schedule: W5-7 PM, room 4-145
Description
What is the value of your research beyond publishing the next paper? Who will your work impact and how is it measured? Students will apply learnings to their own research projects as part of their degree programs.
Learn how to better define and articulate the problems your research addresses, identify stakeholders to validate needs and solutions, and be specific about how the impact might be realized.
Deliverables in the course include 3 ways of describing a project’s impact: An “impact statement” (1-3 sentences), “impact storyline” (one page overview), and “impact case” (5 minute presentation).
Practice leadership skills including aligning toward a vision, communication, giving & receiving feedback, and presenting.
This course counts toward the Graduate Certificate in Technical Leadership.
More information can be found here.
6.S893 Multi-agent communication
- Level: G
- Units: 2-0-10
- Prereqs: 6.8610 or permission of instructor
- Schedule: T2-4, room 36-153
- Instructor: Prof. Jacob Andreas, (jda@mit.edu)
- Satisfies: II; AAGS, Concentration subject in AI
Description
Seminar on problems at the intersection of language processing and multi-agent systems. Topics to be covered include computational models of pragmatic reasoning, language evolution, and “self-play” training for language models..
6.S895 Quantum Cryptography
- Level: G
- Units: 3-0-9
- Prereqs: 6.6410 or 6.6420 and 6.5620 or permission of instructors
- Schedule: TR11-12:30, room 45-102
- Instructor: Prof. Anand Natarajan (anandn@mit.edu), Prof. Vinod Vaikuntanathan (vinod.nathan@gmail.com)
- Satisfies: 6-3 Theory Track, AAGS, Concentration subject in Theoretical CS; EE Track in Quantum System Engineering
Description
An introduction to the many ways quantum computing and cryptography intersect. Topics will include uniquely quantum cryptographic primitives such as quantum key distribution and quantum money, post-quantum cryptography (classical cryptography that is secure against quantum attackers), and the use of cryptography in verifying quantum devices, as well as speculative connections to fundamental physics. Some familiarity with both quantum computing and cryptography is assumed.
6.S950 Global Business of Quantum Computing (meets with 15.S20)
- First half of term
- Level: G
- Units: 2-0-1
- Prereqs:
- Instructor: Professors William Oliver (william.oliver@mit.edu), and Jonathan Ruane (jruane@mit.edu)
- Schedule: W4-5:30, room E62-223
Description
Quantum Computing (QC) offers the potential to solve certain types of problems for human kind; problems that are today, prohibitive for traditional computing. It could lead to exciting breakthroughs in areas such as improved efficiency in logistics chains, increased battery performance for cars or helping to find new pharmaceutical treatments. But what is hype and what is realistic given the development of the field in recent years and its current trajectory? What role do scientists, engineers, managers, entrepreneurs, policy makers and other stakeholders play? This course provides multiple viewpoints including academic, industry and governmental. You will hear from leading MIT faculty and pioneering practitioners in the field. We will demystify topics such as trapped ion and superconducting qubits.
6.S953 Embodied Intelligence
- Level: G
- Units: 3-0-9
- Prereqs: Prerequisites: 6.3900 [6.036] or 6.7900 [6.867] and 6.1210 [6.006] or 6.1220 [6.046]
- Recommended: one of 6.4100, 6.4120, 6.8110, 6.7920
- Instructor: Prof. Phillip Isola (phillipi@mit.edu)
- Schedule: TR1-2:30, room 36-112
- Satisfies: II, AAGS; Concentration subject in AI; EE Track in System Science
Description
Our goal is to address the broad problem of designing a general-purpose intelligent robot. To address this question, a very broad background is required. We propose to spend the first 4 weeks covering important background topics: each week will combine a foundational tutorial reading with one or two state-of-the-art research results. In the subsequent weeks, we will address topics that cut across the basic disciplinary boundaries, typically combining two or more areas, with the work focused on studying and discussing current research in the relevant area.
6.S963 Beyond Models – Applying Data Science/AI Effectively
- Second half of term
- Level: G
- Units: 2-1-3
- Prereqs: 6.3900 or 6.4100 or similar study of machine learning and 18.05 or similar study of statistics
- Instructor: Dr. Alfred Z. Spector, Visiting Scholar (alfreds@mit.edu)
- Schedule: MW2:30-4, room 36-153
- Satisfies: EECS elective
Description
Beyond Models presents the breadth of considerations needed to apply data science and artificial intelligence successfully. Students will learn the landscape of challenges, a unique rubric for systematically evaluating them, and then see the rubric’s application to a variety of case studies. Through a combination of readings including the 2022 book, (Data Science in Context, Foundations, Challenges, and Opportunities), lectures, and in-class discussions, students will delve deeply into seven sets of implementation- and requirements-oriented challenges: from data gathering to meeting ethical, legal, and societal needs. Students will write a short (5-10 pages but carefully crafted) paper individually or in groups of two undertaking a careful analysis of a complex application of data science/ML, aiming at post-class publication on a web-site. The instructor will advise the students on their projects during small, custom-scheduled recitation sections. This class will provide students with additional skills needed to perform/lead successful data science/ML efforts (as data scientists, engineers, or product managers), and it will provide better understanding of future opportunities in research, business, and public policy. Enrollment is limited and class attendance/participation required.
6.S966 Symmetry and its Application to Machine Learning and Scientific Computing
- Level: G
- Units: 3-0-9
- Prereqs: 18.06 or 18.061, 6.100A, 6.1210
- Instructor: Professor Tess Smidt, (tsmidt@mit.edu)
- Schedule: MW2:30-4, room 56-154
- Satisfies: AAGS; Concentration in AI or Numerical Methods or Applied Physics; 6-4 grad_AUS
Description
Introduces the use of group representation theory to construct symmetry-preserving algorithms for machine learning. Emphases the connection between topics in math and physics and machine learning. Students will implement core mathematical concepts in code to build algorithms that can operate on graphs, geometry, scientific data, and other structured data to preserve the symmetries of these domains. Topics covered include: Euclidean and permutation groups, group representations: regular, reducible, and irreducible, tensor products, statistics and sampling of group representation vector spaces, and symmetry-breaking mechanisms.
6.S977 Ethical Machine Learning In Human Deployments
- Level: G
- Units: 3-0-9
- Prereqs: 6.7900
- Schedule: Friday 10-1, room 45-102
- Instructor: Professor Marzyeh Ghassemi (mghassem@csail.mit.edu)
- Satisfies: Concentration Subject in AI, AAGS, grad_II
Description
This course focuses on the human-facing considerations in the pipeline of machine learning (ML) development in human-facing settings like healthcare, employment, and education. Students will learn about the issues involved in ethical machine learning, including framing ethics of ML in healthcare through the lens of social justice. Students will read papers related to ongoing efforts and challenges in ethical ML, ranging from problem selection to post-deployment considerations. Guest lectures will be given by experts in data access, ethics, fairness, privacy and deployments, and the course will focus around a central project that students will use to explore how machine learning can potentially be brought into human-facing deployments ethically.
More information can be found at
https://canvas.mit.edu/courses/25591/pages/6-dot-s977-ethical-machine-learning-in-human-deployments
6.S984 Datacenter Computing
- Level: G
- Units: 3-0-9
- Prereqs: 6.1910 or equivalent and 6.1800 or equivalent
- Schedule: TR1-2:30, room 5-134
- Instructor: Prof. Christina Delimitrou, (cdel@mit.edu)
- Satisfies: II, AAGS; Concentration subject in Computer Systems; EE Track & CS Track in Architecture
Description
Warehouse-scale datacenters host a wide range of online services, including social networks, web search, video streaming, machine learning, and serverless workloads. In this course, we will study the end-to-end stack of modern datacenters, from hardware and OS all the way to resource managers and programming frameworks. We will also explore cross-cutting issues, such as total cost of ownership, service level objectives, availability, and reliability. The course is a combination of lectures and paper readings. Students will read up to two papers per topic and submit brief summaries. During class meetings, we will start with a student presentation of the papers followed by an in-class discussion. The main deliverable for the course is a semester-long group project which should address an open research problem in modern cloud environments (project suggestions will be provided by the instructor, but students are also welcome to propose their own).
The class is appropriate for graduate and advanced undergraduate students who want to learn more about cloud computing and datacenter systems.
6.S986 Large Language Models and Beyond
- Level: G
- Units: 2-0-10
- Prereqs: 6.8610/6.8611, 6.3900, 6.7900
- Instructor: Prof. Yoon Kim
- Schedule: Tuesdays, 11-1, first meeting room 32-082
- Satisfies: AAGS, Concentration subject in AI
Description
The field of natural language processing has recently made remarkable progress through large language models trained at scale on broad data. This seminar will explore various issues and questions surrounding these systems, including: applications (in NLP and other domains), emergent capabilities, efficient training & deployment, philosophical and ethical implications, and more.
6.S988 Mathematical Statistics: A Non-Asymptotic Approach (meets with IDS.160, 9.521, 18.656)
- Level: G
- Units: 3-0-9
- Prereqs: 6.7700 and 18.06 and 18.6501, or permission on instructor
- Instructor: Professors Martin Wainwright (mjwain@mit.edu) and Alexander Rakhlin (rakhlin@mit.edu)
- Schedule: TR1-2:30, room 1-150
- Satisfies: AAGS, Concentration in AI
Description Introduces students to modern non-asymptotic statistical analysis. Topics include high-dimensional models, nonparametric regression, covariance estimation, principal component analysis, oracle inequalities, prediction and margin analysis for classification. Develops a rigorous probabilistic toolkit, including tail bounds and a basic theory of empirical processes.