CS 449 Learning for Robotics
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Fundamental ideas and techniques for constructing intelligent (robotic) systems acting in the world. Perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based). Deep reinforcement learning approaches (policy gradients, actor-critic algorithms, value function and q-function methods, inverse reinforcement learning, meta-learning). Highlights of state-of-the-art methods and application domains.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
(CS 102 or CS 114 or CS 115) and (MATH 225 or MATH 220 or MATH 224 or MATH 241) and (MATH 230 or MATH 255 or MATH 260).
Autumn Semester (Özgür Salih Öğüz)
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