Geometric algebra for applications in signal and image processing, computer vision, graphics engineering, robotics and machine learning. Prof. Eduardo Bayro-Corrochano Camera Culture Group, Media Lab. MIT Course of 8 to 10 lectures on Wednesdays 15:00-17:00 Begins in October Handouts G 0° OO 90 O e 0 cio O° Resume Gvb i:47,„yt In this course we present the framework of geometric algebra for applications in signal and image processing, computer vision, graphics engineering, robotics and machine learning. We will show that this mathematical system keeps our intuitions and insight of the geometry of the problem at hand and it helps us to reduce considerably the computational burden of the problems. We believe that the framework of geometric algebra can be in general of great advantage for applications in signal and image processing, filtering, estimation and interpolation, neural networks and machine learning, PCA and big data, graphics engineering, stereo vision, range data, laser, stereo-omnidirectional and odometry based robotic systems, kinematics, dynamics and nonlinear control of robot mechanisms, robot manipulators, mobile robots and humanoids. Content 1. Introduction to Geometric Algebra 1.1 Introduction to Associative Algebras 1.2 History of Geometric Algebra 1.3 Introduction to Geometric Algebra 1.4 Algebra of the 2D and 3D Spaces 1.5 Motor Algebra 1.6 Projective Geometry , Algebra of Incidence and Invariant Theory 1.7 Conformal Geometric Algebra 1.8 Computer Programming Issues EFTA01087827