The following subjects will be offered:
6.5060 Algorithm Engineering (late offering for fall 2025)
- Level: G
- Units: 3-0-9
- Prereqs: 6.1060 and 6.1220
- Schedule: TR 11-12:30, room 34-304
- Instructor: Prof. Julian Shun (jshun@mit.edu)
- Satisfies: Concentration subject in Computer Systems or Theoretical CS; AAGS; EECS; CS Track in Programming Principles and Tools; Theory; EE Track in Hardware and Software
Description
Covers the theory and practice of algorithms and data structures. Topics include models of computation, algorithm design and analysis, and performance engineering of algorithm implementations. Presents the design and implementation of sequential, parallel, cache-efficient, and external-memory algorithms. Illustrates many of the principles of algorithm engineering in the context of parallel algorithms and graph problems.
6.S042/6.5820 Computer Networks
- Level: Undergraduate/Graduate
- Units: 3-0-9
- Prereqs: 6.1800 or 6.1810, or permission of instructor
- Schedule: MW11-12:30, room 6-120
- Instructors: Professors Hari Balakrishnan (hari@csail.mit.edu), Mohammad Alizadeh (alizadeh@mit.edu)
- Satisfies: AUS; 6-3 Systems Track; Concentration Subject in Computer Systems
Description
Topics on the engineering and analysis of network protocols and architecture, including architectural principles for designing heterogeneous networks; transport protocols; Internet routing; router design; congestion control and network resource management; wireless networks; network security; naming; overlay and peer-to-peer networks. Readings from original research papers. Semester-long project and paper. Students taking graduate version complete different assignments.
6.S043/6.S983 AI and Decision Making in the Medicine: From Disease to Therapy
- Level: U/G
- Units: 3-0-9
- Prereqs: 6.100A AND 6.C01 AND (7.012 OR 7.05 OR 5.07)
- Instructors: Profs. Regina Barzilay (regina@csail.mit.edu) and Collin Stultz (cmstultz@mit.edu)
- Schedule: TR 11-12:30, room 56-114
- Satisfies: AAGS; EECS; CI-M; 6-4 Application CI-M; Concentration subjects in BioEE or AI;
Description
Introduction to fundamental principles and applications of artificial intelligence (AI) in medicine and medical research. Students are introduced to foundational concepts in machine learning as it pertains to clinical decision support systems, personalized medicine, and advanced computational methods for drug optimization and protein folding. The role of explainablity and uncertainty analysis in deep learning for healthcare are discussed. Problem sets integrate theoretical knowledge and hands-on applications based on concrete problems in both medical and pharmaceutical science.
6.S044 AI and Rationality (also offered under 24.S00)
- Level: U
- Units: 3-0-9
- Prereqs: 6.1200 or a subject in probability or logic
- Instructors: Prof. Leslie Kaelbling (lpk@mit.edu) and Prof. Brian Hedden (bhedden@mit.edu)
- Schedule: MW11-12:30, room 66-144
- Satisfies: EECS elective; 6-4 Center: Human-centric
Description
This class brings together philosophical work on rationality – primarily in formal epistemology and decision theory – with work in AI on belief-formation and planning, with the aim of giving students interested in AI a more philosophically sophisticated understanding of rational agency and illustrating how attempts to design intelligent systems might shed light on human rationality. The distinction between “ideal” rationality and “bounded” rationality (which takes computational and memory limitations into account) will be a major theme throughout the course.
We begin with an overview of theories of rationality and agency, including classic discussions of whether it can make sense to attribute mental states like beliefs, desires, and intentions to artificial systems. We then focus on different aspects of rational agency – beliefs, desires/utilities, decision theory, planning and sequential decision-making, and multi-agent cases – before closing the course with a week on whether LLMs have beliefs and desires.
6.S056 Hack Yourself: Data-driven Wellbeing and Learning (also offered under ES.S70)
- Level: U
- Units: 3-0-9
- Prereqs: 6.1000 or 6.100A
- Instructors: Ana Bell, EECS Senior Lecturer (anabell@mit.edu), Paola Rebusco, ESG Senior Lecturer (pao@mit.edu), Carter Jernigan ’07, MAPP, Andreas Karatsolis, Associate Director WRAP (karatsol@mit.edu)
- Schedule: Friday 2-4, room 24-115, Recitations: Tuesdays 2, room 24-307
- Satisfies: 6-3 Track: Computers and Society
Description
Did you know that celebrating a friend’s success is more important than supporting them during tough times? Or that taking a practice test improves memory 17% more than simply studying? Or that time pressure decreases your creativity by 45%?
Psychology is full of surprises, but simply knowing the facts isn’t enough. In this course, you’ll take charge of your wellbeing and learning and develop leadership skills, coming away with a toolkit of over 60 sustainable positive habits. In addition, you’ll explore data science methods to validate these positive psychology tools, gaining hands-on experience with AI and statistical analysis that you will be able apply across different fields. Finally, you’ll develop skills for positive communication and effective persuasion through data and visualization.
6.S061 Humane User Experience Design (meets with 21A.S02 (U) and 21A.S10 (G))
- Level: U
- Units: 3-1-8
- Prereqs: 6.1010
- Instructors: Profs. Arvind Satyanarayan (arvindsa@mit.edu) and Graham Jones (gmj@mit.edu), Anthropology
- Schedule: MW9:30-11, room 36-155
- Satisfies: II; 6-3 Track: HCI; 6-4 Center: Human-centric
Description
Teaches an end-to-end user-centric design process focusing on developing humane (usable, joyful, whimsical) frontend user experiences for generative AI, grounded in anthropology. Topics include understanding human context (e.g., through observation, interviews, and user testing), qualitative data analysis, principles of effective and expressive visual and interaction design, frontend web application implementation, and user testing and ethical audits. Enrollment limited.
6.S890 Topics in Multiagent Learning
- Level: G
- Units: 3-0-9
- Prereqs; 6.1220 or 6.7201, 6.1200
- Instructors: Profs. Gabriele Farina (gfarina@mit.edu), Costis Daskalakis (costis@csail.mit.edu)
- Schedule: TR11-12:30, room 3-333
- Satisfies: 6-3 Theory Track, Concentration subject in AI or Theory; AUS; II; 6-4 AUS; AAGS
Description
While machine learning techniques have had significant success in single-agent settings, an increasingly large body of literature has been studying settings involving several learning agents with different objectives. In these settings, standard training methods, such as gradient descent, are less successful and the simultaneous learning of the agents commonly leads to nonstationary and even chaotic system dynamics.
Motivated by these challenges, this course presents the foundations of multi-agent systems from a combined game-theoretic, optimization and learning-theoretic perspective, building from matrix games (such as rock-paper-scissors) to stochastic games, imperfect information games, and games with non-concave utilities. We will present manifestations of these models in machine learning applications, from solving Go to multi-agent reinforcement learning, adversarial learning and broader multi-agent deep learning applications. We will discuss aspects of equilibrium computation and learning as well as the computational complexity of equilibria. We will also discuss how the different models and methods have allowed several recent breakthroughs in AI, including human- and superhuman-level agents for established games such as Go, Poker, Diplomacy, and Stratego. A tentative course syllabus can be found below.
More information can be found at: https://mit.edu/~gfarina/www/6S890
6.S892 Advanced Topics in Power Electronics
- Level: G
- Units: 3-0-9
- Prereqs: 6.6620 or equivalent
- Instructors: Prof. David J. Perreault (djperrea@mit.edu)
- Schedule: TR1-2:30, room 24-121
- Satisfies: AAGS; 6-5 Energy Track; Concentration subject in Applied Physics
Description
Advanced topics in analysis, design, manufacturing, and control of power
electronics. Topics include: architectures and topologies for power electronics including soft-switched, resonant, high-frequency and switched-capacitor circuits; high-frequency power magnetics; advanced modeling and control methods; power components, devices, and materials; manufacturing methods; low-noise circuit design and filtering; circuit theory applications to power electronics; emerging technologies and applications in power conversion.
6.S894 Accelerated Computing
- Level: G
- Units: 1-2-9
- Prereqs: 6.1060, 6.1910
- Instructors: Prof. Jonathan Ragan-Kelley (jrk@mit.edu)
- Schedule: Lec: Th2:30-3:30, room 32-155; Labs T2:30-4:30, room 32-155
- Satisfies: AAGS; Concentration subject in Computer Systems or Graphics and HCI
Description
Project-based introduction to software performance engineering on specialized hardware and accelerators. Topics include fundamentals of accelerator architecture from a performance engineer’s perspective, accelerator programming models and tools, and analysis of accelerator performance.
6.S896 Algorithmic Statistics
- Level: G
- Units: 3-0-9
- Prereqs: 6.1220 and 18.600 and 18.200 or equivalents, and grad-level mathematical maturity
- Instructors: Prof. Sam Hopkins (samhop@mit.edu)
- Schedule: MW2:30-4, room 32-124
- Satisfies: II; 6-3 CS Track: Theory; AAGS; Concentration subject in Theory or AI
Description
Introduction to algorithms and computational complexity for high-dimensional statistical inference problems, with focus on provable polynomial-time guarantees. Covers modern algorithm design techniques via convex programming and Sum of Squares method, graphical models as a language to describe complex but tractable high-dimensional learning problems and associated learning algorithms, and basics of complexity for statistical problems, including statistical query and low-degree lower bounds and reductions.
6.S965 Digital Systems Laboratory II
- Level: G
- Units: 3-7-2
- Prereqs: 6.2050
- Instructors: Joe Steinmeyer, Senior Lecturer, EECS (jodalyst@mit.edu)
- Schedule: MW3:30-5, room 36-112
- Satisfies: DLAB; II; 6-2 PLAB; AAGS; 6-2 EE Track: Architecture, Device, Circuits, Systems, Embedded Systems, Hardware Design, Hardware and Software; Concentration subject in Circuits or Computer Systems, or Communications
Description
Continuation of topics introduced in Digital Systems Laboratory (6.205). Particular focus on modern design verification practices, System Verilog, and Universal Verification Methodology (UVM) as well as designing complex digital systems in hybrid platforms such as SoC and state-of-the-art RFSoC platforms. Weekly labs and final design project with emphasis on signal processing, RF, data acquisition, and other applications. The course will utilize a number of tools and areas of study in pursuit of our work, reflective of the hybrid modern state of the field, so usage of C, Python, shells scripts, signals and systems, communications, and RF concepts, should be expected.
6.S981 Special Seminar: AI for Protein Biology
- Level: G
- Units: 3-0-9
- Prereqs: permission of instructor
- Instructors: Professor Alexander Rives, (arives@mit.edu)
- Schedule: T1-4, room 26-210
- Satisfies: EECS elective
- Enrollment limited
Description
Enrollment limited. Graduate seminar on artificial intelligence for protein understanding and design. Explores the developing understanding of protein biology with artificial intelligence, building from classical ideas about the structure and organization of protein space, and making connections to foundational modeling approaches across artificial intelligence from representation learning, to language models, diffusion models, and mechanistic interpretability. It also surveys the frontier of applications of artificial intelligence to programmable biology.