Subject Updates Fall 2024

    The following subjects will be offered:

    6.C40/24.C24 Ethics of Computing (NEW)

    Description

    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.

    6.C57 (G)/6.C571 (U) Optimization Methods (previously 6.7201/6.7200)

    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.

    6.7960 Deep Learning (NEW was 6.S898)
    • Level: Graduate
    • Units: 3-0-9
    • Prereqs: 18.05 and (6.3720, 6.3900, or 6.C01)
    • Schedule: TR1-2.30 (45-230)  
    • Instructor: Prof. Phillip Isola (phillipi@mit.edu)
    • Satisfies: AUS; 6-4 AUS; AAGS; Concentration subject in AI;

    Description
    Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics.

    6.S053/6.S899 Multiphysics Systems Modeling (MSM)
    • Level:  Undergraduate/Graduate
    • Units:  2-1-9
    • Prereqs:  20.330 or 6.4810; or permission of instructor
    • Schedule:  TBD
    • Instructors:  Prof. Jongyoon Han (jyhan@mit.edu)
    • Satisfies:  AUS

    Description

    Practices the use of modern numerical analysis tools (e.g., COMSOL) for various multiphysics systems, such as biosensors, microfluidic devices, and other systems involving multiple phenomena. Covers modeling of diffusion, reaction, convection and other transport mechanisms, as well as electromagnetic fields. Discusses both fundamental insights and practical issues and challenges in numerical modeling. No prior knowledge of modeling software is necessary. Evaluations are done by weekly modeling homework and open-ended modeling projects. No exams.

    6.S890 Topics in Multiagent Learning
    • Level:  Graduate              
    • Units:  3-0-9
    • Prereqs: 6.1220 or 6.7201, 6.1200
    • Schedule:  TR11-12:30 (3-333)
    • Instructors:  Prof. Gabriele Farina (gfarina@mit.edu)
    • Satisfies: AUS2, II, 6-4 AUS, AAGS, 6-3 Track in Theory, Concentration subject in Theoretical CS, AI

    Description

    Presents research topics at the interface of computer science, machine learning, and game theory. Explores computational aspects of strategic behavior for decision-makers in nonstationary multiagent environments. Explores game-theoretic notions of optimality that are applicable to these settings, and how decision-makers may learn optimal strategies from repeated interaction. Presents equilibrium computation algorithms, complexity barriers for equilibria and fixed points, the theory of learning in games, and multi-agent reinforcement learning. Presents practical aspects of learning in games, including recent progress in solving Go, Poker and other large games. 

    6.S965 Digital Systems Laboratory II
    • Level:  Graduate
    • Units:  3-7-2
    • Prereqs:  6.2050
    • Schedule:  TBD
    • Instructors:  Joseph D. Steinmeyer (jodalyst@mit.edu), EECS Lecturer
    • Satisfies: II, DLAB, 6-2 PLAB, AAGS

    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.S974 Fixed Parameter and Fine-grained Complexity (meets with 6.1420)
    • Level:  Graduate
    • Units:  3-0-9
    • Prereqs:  6.1200, 6.1210, and (6.1220, 6.1400, or 18.404)
    • Schedule: TR11-12.30 (34-304)
    • Instructors:   Prof. Ryan Williams (rrw@mit.edu) and Prof. Virginia Williams (virgi@mit.edu)
    • Satisfies:  AUS2, II, AAGS, grad_AUS2, Concentration in Theoretical Computer Science

    Description

    An overview of the theory of parameterized algorithms and the “problem-centric” theory of fine-grained complexity, both of which reconsider how to measure the difficulty and feasibility of solving computational problems. Topics include: fixed-parameter tractability (FPT) and its characterizations, the W-hierarchy (W[1], W[2], W[P], etc.), 3-sum-hardness, all-pairs shortest paths (APSP)-equivalences, strong exponential time hypothesis (SETH) hardness of problems, and the connections to circuit complexity and other aspects of computing.