‣	ROBT 407 introduces students to advanced analytical tools and techniques in machine learning. The course covers supervised and unsupervised learning, neural networks, deep learning, support vector machines, decision trees, linear discrimination, and kernel-based learning methods. It also explores machine learning experiment design. Students engage in term projects using Python-based machine learning packages such as Scikit-learn, PyTorch, NumPy, SciPy, Pandas, and Matplotlib, as well as online databases.
‣	ROBT 206 is the introductory course in the Robotics and Mechatronics Program. This 8-credit course covers logic, computer system design, and microcontroller programming. Key topics include Boolean algebra, circuit design, instruction sets, and microcontroller peripherals. Students will use various software—such as Arduino IDE, MATLAB, Quartus, and Proteus—to simulate logic circuits. Laboratory sessions offer hands-on programming experience with FPGA boards and electronic components.
‣	This course offers a comprehensive overview of Brain-Machine Interface (BMI) technology and its applications. The curriculum covers both invasive and non-invasive BMI systems, exploring their use in controlling devices like user interfaces, prosthetic arms, wheelchairs, and robotic exoskeletons. It also examines how BMI technology can help patients with locked-in syndrome regain movement. The course discusses non-clinical applications of BMI, including security, monitoring, entertainment, gaming, and education. Through lectures, case studies, research paper discussions, and hands-on demonstrations, participants will gain practical experience with BMI systems. Students will also learn neural signal processing and machine learning algorithms using Python libraries.
https://www.youtube.com/@mlearnxyz/	YouTube Channel