(ROBT407) Machine Learning with Applications

<aside> <img src="/icons/book_gray.svg" alt="/icons/book_gray.svg" width="40px" /> 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, machine learning experiment design, decision trees, linear discrimination, and kernel-based learning methods. It also includes term projects where students use Python-based machine learning packages like Scikit Learn, Pytorch, Numpy, Scipy, Pandas, Matplotlib, and online databases.

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(ROBT206) Microcontrollers with Lab

<aside> <img src="/icons/book_gray.svg" alt="/icons/book_gray.svg" width="40px" /> ROBT 206 is the introductory course in the Robotics and Mechatronics Program. This 8-credit course encompasses logic, computer system design, and microcontroller programming. Key topics include Boolean algebra, circuit design, instruction sets, and microcontroller peripherals. Various software, such as Arduino IDE, MATLAB, Quartus, and Proteus, will simulate logic circuits. Laboratory sessions will offer hands-on programming experience using FPGA Boards and electronic components.

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(ROBT613) Brain-Machine Interfaces

<aside> <img src="/icons/book_gray.svg" alt="/icons/book_gray.svg" width="40px" /> This course provides a comprehensive overview of Brain-machine interface (BMI) technology and its applications. The curriculum includes the study of both invasive and non-invasive BMI systems that can control devices such as user interfaces, prosthetic arms, wheelchairs, and robotic exoskeletons. An exploration of how BMI technology can aid patients with locked-in syndrome in regaining movement is also included. Non-clinical uses of BMI technology, such as security, monitoring, entertainment, gaming, and education, are discussed. The course format includes lectures, case studies, research paper discussions, and hands-on demonstrations of BMI systems. Participants will learn about neural signal processing and machine-learning algorithms using Python libraries.

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