Artificial Intelligence and Machine Learning for Engineering Design
Machine learning and artificial intelligence techniques in engineering design applications. Emphasizes state-of-the-art machine learning techniques to design new products or systems or solve complex engineering problems. Lectures cover the theoretical and practical aspects of machine learning and optimization methods. Challenge problems, research paper discussions, and interactive in-class activities are used to highlight the unique challenges of machine learning for design applications. A group term project on students' applications of interest. Basic programming and machine learning familiarity are recommended. Students taking graduate version complete additional assignments.
Fall 2003 · Dynamics of Nonlinear Systems
Topics
- Input/Output and State-Space Models
- Differential Equations as System Models
- Continuous Dependence on Parameters
- Analysis Based on Continuity
- Lyapunov Functions and Storage Functions
- Storage Functions and Stability Analysis
- Finding Lyapunov Functions
- Local Behavior at Eqilibria
- Local Behavior Near Trajectories
- Singular Perturbations and Averaging
- Volume Evolution and System Analysis
- Local Controllability
- Feedback Linearization
Prerequisites & course information
Subject meets with 2.155 Prereq: None G (Fall) 3-0-9 units
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Fall 2003
- MIT OCW syllabus ↗
- MIT OCW calendar ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.