ROBOTICS WITH ML

Introduction to Robotics & Machine Learning – Overview, Applications, and Integration

Basics of Machine Learning – Supervised, Unsupervised, and Reinforcement Learning Concepts

Robot Kinematics – Forward & Inverse Kinematics, Motion Planning

Sensors & Actuators – Types, Working Principles, and Data Collection for ML Models

Robot Programming – Python, ROS (Robot Operating System), and ML Libraries

ML Algorithms in Robotics – Regression, Classification, Clustering, and Reinforcement Learning

Computer Vision for Robots – Image Processing, Object Detection, Recognition, and Tracking

Path Planning & Navigation – Using ML for Obstacle Avoidance and Optimal Path Selection

Simulation & Testing – MATLAB, Gazebo, or Other Robotics Simulation Tools for ML Applications

Applications & Future Trends – Autonomous Vehicles, Industrial Automation, Collaborative Robots, AI-Driven Robotics