COURSE COORDINATOR: Prof. Brenna Argall

PREREQUISITES: Graduate-level standing (or permission of instructor) for the maths, some programming experience (in Matlab okay).

I. Introduction
   1. Crash course in robotics: sensors and sensing, effectors and actuators
   2. Probability basics

II. State estimation and uncertainty filters
   1. Bayes filters
   2. Gaussian filters : Kalman, Information...
   3. Particle filters

III. Machine Learning
   1. Bayesian Learning : Bayes rule, Bayes classifier, MAP, MLE, EM, Mixtures of Gaussians...
   2. Linear classifiers : perceptron, winnow...
   3. Experts style:voting, bandits...
   4. Programming: Linear, Quadratic, Convex
   5. Genetic Algorithms
   6. InstancebasedLearning : nearest neighbors, regression (linear, locally weighted, kernel)...
   7. Reinforcement Learning : Bellman, Qlearning,TDlearning, actorcritic...

IV. Artificial Intelligence
   1. Search
     1. Uninformed
     2. Informed : Greedy, A*, D*, heuristic functions...
     3. Local/optimizing : gradient descent, hill climbing, simulated annealing...
 
   2. Behavior based robotics : reactive, subsumption architecture, hierarchical control...

V. Special topics

WEEKLY SCHEDULE

  • 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

more news