Description and classification of robots. A general view of mechanics and kinematics for joints, links and gripper. Inverse kinematics. Determination of dynamical models. State-space representation and linearization of nonlinear models. Control of robots. Independent joint control. Force control. Trajectory planning and control.

Overview of learning and statistical decision theory. Model inference and parameter estimation. Linear models for regression and classification. Kernel methods. Nonparametric methods. Model assessment and selection. Ensemble methods. Unsupervised learning.

Extraction of low-level features, boundary and region based analysis, segmentation and grouping, lightness and color, shape from shading. Photometric and binocular stereo, optical flow and motion estimation, strongly-modeled vision, weakly-modeled vision recognition, integration and vision systems, real-time vision.

Calculus of variations in system optimization. Two point boundary value problems. Optimal control function and optimal control law. Dynamic programming. Pontryagin's Minimum Principle: minimum time, minimum fuel, minimum energy problems. Optimal control design with quadratic criteria. Regulation and tracking problems. Singular control problems.