Courses / DescriptionsEECS 469: Machine Learning and Artificial Intelligence for Robotics
Quarter OfferedFall : 11-12:30 TuTh ; Argall
PrerequisitesGraduate-level standing (or permission of instructor) for the maths, some programming experience (in Matlab okay).
A coverage of artificial intelligence, machine learning and statistical estimation topics that are especially relevant for robot operation and robotics research. The focus is on robotics-relevant aspects of ML and AI that are not covered in depth in EECS 348 or EECS 349. Course evaluation will be largely project-based.
- This course fulfills the AI Depth requirement.
COURSE COORDINATOR: Prof. Brenna Argall
PREREQUISITES: Graduate-level standing (or permission of instructor) for the maths, some programming experience (in Matlab okay).
REQUIRED TEXTS: Xandu has created a special textbook comprised of custom reprints, available at the bookstore.
DETAILED COURSE TOPICS:
I. Introduction: Crash course in robotics: sensors and sensing, effectors and actuators, probability basics
II. State estimation and uncertainty filters
1. Bayes filters
2. Gaussian filters : Kalman, Information...
3. Nonparametric filters: Histogram, Particle...
III. Machine Learning
1. Neural Nets : perceptron, multi-layered networks...
2. Genetic Algorithms
3. Instance-based Learning : nearest neighbors, regression (linear, locally-weighted, kernel-based)...
4. Reinforcement Learning : Bellman, Q-learning, T-D learning, actor-critic...
5. Demonstration-based Learning
IV. Artificial Intelligence
2. Informed : Greedy, A*, D*, heuristic functions...
3. Local/optimizing : gradient descent, hill-climbing, simulated annealing...
- Week 0 : Introduction
- Week 1 : State estimation and uncertainty filters
- Week 2 : ML: Bayesian Learning, Linear Classifiers, Expertsstyle
- Week 3 : ML: Programming, Genetic Algorithms
- Week 4 : ML: InstancebasedLearning
- Week 5 : ML: Reinforcement Learning
- Week 6 : AI: Planning
- Week 7 : AI: Search, BehaviorbasedRobotics
- Week 8 : Project presentations
- Week 9 : Project presentations, Special topics