Robotics: Reasoning Core Area Course List

The following courses can satisfy the Reasoning Core Area Breadth Requirement for the Robotics MS/PhD program:

  • AEROSP 552: Aerospace Information Systems
  • AEROSP 575: Flight & Trajectory Optimization
  • AEROSP 584: Avionics, Navigation & Guidance of Aerospace Vehicles
  • AEROSP 584: Navigation & Guidance of Aerospace Vehicles
  • AEROSP 729/NERS 590/MECHENG 599: Machine Learning for Science
  • EECS 453: Principles of Machine Learning – *EECS 543 OR EECS 545 (not both) can count toward the Robotics MS and/or PhD
  • EECS 486: Information Retrieval & Web Search
  • EECS 498: Principles of Machine Learning (credit only if taken before EECS 545 and EECS 553)
  • EECS 498: Introduction to Algorithmic Robotics
  • EECS 505: Computational Data Science and Machine Learning (Nadakuditi)
  • EECS 542: Advanced Topics in Computer Vision
  • EECS 543: Knowledge-Based Systems
  • EECS 545: Machine Learning – *EECS 543 OR EECS 545 (not both) can count toward the Robotics MS and/or PhD 
  • EECS 548: Information Visualization
  • EECS 549 / SI 650: Information Retrieval
  • EECS 550: Information Theory
  • EECS 558: Stochastic Control 
  • EECS 559: Optimization for Signal Processing and Machine Learning
  • EECS 563: Hybrid Systems: Specification, Verification, & Control
  • EECS 576: Advanced Data Mining
  • EECS 592: Foundations of Artificial Intelligence
  • EECS 595/LING 541/SI 561: Natural Language Processing 
  • EECS 598: Foundations of Large Language Models
  • EECS 598: Machine Learning Theory
  • EECS 602: Reinforcement Learning Theory (Ying)
  • EECS 692: Advanced Artificial Intelligence
  • IOE 434: Human Error and Complex System Failures
  • IOE 465: Design and Analysis of Experiments
  • IOE 511: Continuous Optimization Methods
  • IOE 512: Dynamic Programming
  • IOE 536: Cognitive Ergonomics and Human System Integration
  • IOE 560: Bayesian Data Analysis
  • IOE 562: Reliability
  • IOE 611: Nonlinear Programming
  • IOE 691: Approximation Algorithms
  • MATH 525: Probability Theory
  • MECHENG 555: Design Optimization
  • ROB 511: Robot Operating Systems
  • ROB 520 (formerly EECS 598): Motion Planning
  • ROB 543: Ethics in AI & Robotics

Special Topics Courses:

  • EECS 498: Principles of Machine Learning *credit only if taken before EECS 545 and EECS 553
  • EECS 498: Machine Learning Basics for Engineering Applications * credit only if taken before 545 and EECS 553
  • EECS 598: Reinforcement Learning *Seating is extremely limited. Consider alternate courses.
  • EECS 598: Computational Data Science
  • EECS 598: Computational Modeling in Human-Computer Interaction
  • EECS 598: Situated Language Processing for Embodied AI Agents
  • EECS 598: Formal Verification
  • EECS 598: Approximation Algorithms