Module #1 Introduction to Robotics and Machine Learning Overview of robotics and machine learning, their intersection, and importance in modern robotics
Module #2 Robotics Fundamentals Review of robotics concepts:sensors, actuators, kinematics, dynamics, and control systems
Module #3 Machine Learning Basics Introduction to machine learning:supervised, unsupervised, and reinforcement learning, regression, classification, and clustering
Module #4 Python for Robotics and Machine Learning Python programming for robotics and machine learning, including necessary libraries and tools
Module #5 Data Acquisition and Preprocessing Collecting and preprocessing data for machine learning in robotics:sensor data, data types, and feature extraction
Module #6 Supervised Learning for Robotics Applying supervised learning to robotics:classification, regression, and neural networks for robot control and perception
Module #7 Unsupervised Learning for Robotics Applying unsupervised learning to robotics:clustering, dimensionality reduction, and anomaly detection for robot discovery
Module #8 Reinforcement Learning for Robotics Applying reinforcement learning to robotics:Q-learning, SARSA, and Deep RL for robot control and optimization
Module #9 Deep Learning for Robotics Deep learning techniques for robotics:CNNs, RNNs, and LSTMs for image and signal processing
Module #10 Robot Perception and Computer Vision Machine learning for robot perception:object detection, tracking, and recognition using computer vision
Module #11 Robot Motion Planning and Control Machine learning for robot motion planning and control:motion planning, trajectory optimization, and control policy learning
Module #12 Human-Robot Interaction and Learning Machine learning for human-robot interaction:learning from demonstration, imitation learning, and human-robot collaboration
Module #13 Robot Learning from Demonstration Learning from demonstration:approaches, algorithms, and applications for robot skill acquisition
Module #14 Off-Policy Reinforcement Learning Off-policy reinforcement learning:importance sampling, Q-learning, and gradient-based methods for robot learning
Module #15 Transfer Learning and Domain Adaptation Transfer learning and domain adaptation:adapting machine learning models to new robotic environments and tasks
Module #16 Explainability and Transparency in Robotics Explainability and transparency in robotics:model interpretability, saliency maps, and attention mechanisms
Module #17 Robotics and Machine Learning Ethics Ethical considerations in robotics and machine learning:fairness, accountability, and transparency
Module #18 Specialized Topics in Machine Learning for Robotics Advanced topics in machine learning for robotics:sensorimotor learning, embodied cognition, and cognitive architectures
Module #19 Robotics and Machine Learning Tools and Frameworks Overview of popular robotics and machine learning tools and frameworks:ROS, PyTorch, TensorFlow, and OpenCV
Module #20 Case Studies and Applications Real-world applications of machine learning in robotics:robotic grasping, manipulation, and navigation
Module #21 Project Development and Implementation Guided project development:applying machine learning to a robotics project
Module #22 Project Evaluation and Debugging Evaluating and debugging machine learning models for robotics:metrics, visualization, and troubleshooting
Module #23 Robotics and Machine Learning Research Frontiers Current research frontiers in robotics and machine learning:open challenges and opportunities
Module #24 Future of Robotics and Machine Learning Future directions and trends in robotics and machine learning:potential applications and societal implications
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Robotics career
