Safe and Reliable Artificial Intelligence Systems (2.S999/IDS.S22): Spring 2026
Modern AI systems are increasingly deployed in high-stakes and safety-critical settings, such as robotics, autonomy, manufacturing, healthcare, and scientific computing. For such settings, it is not enough for models to perform well on average; they must also respect constraints, provide calibrated uncertainty estimates, and remain dependable under distribution shift and during closed-loop decision-making. This subject develops a unified toolbox for building machine learning models that are safe and reliable, bridging methods from control theory, optimization, uncertainty estimation, modern generative modeling, and reinforcement learning.
Statistics, Computation, and Applications (IDS.012/2.092/6.3730 & IDS.131/2.093/6.3732): Spring 2025, Spring 2024, Spring 2023
Hands-on analysis of data demonstrates the interplay between statistics and computation. Includes four modules, each centered on a specific data set, and introduced by a domain expert. Provides instruction in specific, relevant analysis methods and corresponding algorithmic aspects. Potential modules may include medical data, gene regulation, social networks, finance data (time series), traffic, transportation, weather forecasting, policy, or industrial web applications. Projects address a large-scale data analysis question.
Numerical Computation (2.086): Fall 2025, Fall 2024, Fall 2023, Fall 2022, Fall 2021
Covers elementary programming concepts, including variable types, data structures, and flow control. Provides an introduction to linear algebra and probability. Numerical methods relevant to MechE, including approximation (interpolation, least squares, and statistical regression), integration, solution of linear and nonlinear equations, and ordinary differential equations. Presents deterministic and probabilistic approaches. Uses examples from MechE, particularly from robotics, dynamics, and structural analysis.
Dynamics and Control II (2.004): Spring 2022
Modeling, analysis, and control of dynamic systems. System modeling: lumped parameter models of mechanical, electrical, and electromechanical systems; interconnection laws; actuators and sensors. Linear systems theory: linear algebra; Laplace transform; transfer functions, time response and frequency response, poles and zeros; block diagrams; solutions via analytical and numerical techniques; stability. Introduction to feedback control: closed-loop response; PID compensation; steady-state characteristics, root-locus design concepts, frequency-domain design concepts. Laboratory experiments and control design projects.
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