Students completing "Introduction to Robotics" will gain a deep
understanding of the hardware and software involved in robotics,
with a focus on programming algorithms. They will understand the
past, present, and future of robotics and the main challenges
that make robotics difficult. Students will learn various
mathematical and statistical models, associated algorithms, and
their implementations which are making modern-day robotics
possible. The course will consist of lectures and breakout
sessions. Students will be graded on the standard A to F grading
scale and will provide course evaluations on the instruction and
course content following established Computer Science evaluation 
procedures.

I. Overview of Robotics
   1. Introduction
   History, State-of-the-art, and Future
   2. Robot Hardware
   Sensors and Effectors
   3. Robotic Software Architectures
   4. Probability Theory
II. Robotic Perception Under Uncertainty
   1. Maps
   2. Range Finders 
   Beam models, Likelihood fields
   3. Cameras
   Feature-based Measurement Models
III.  Robotic Motion Under Uncertainty
   1. Kinematics
   2. Velocity Motion Model
   3. Odometry Motion Model
   4. Motion and Maps
IV. Localization
   1. State Estimation Under Uncertainty
   2. Filters
   Bayes, Kalman, Extended Kalman, and Monte Carlo
   3. Taxonomy of Localization Problems
   4. Markov Localization
   5. Extended Kalman Filter Localization
   6. Grid Localization
   7. Monte Carlo Localization
V. Mapping
   1. Occupancy Grid Mapping
   2. Learning Inverse Measurement Model
   3. SLAM with Extended Kalman Filter