ROBOTICS & MACHINE LEARNING - 4 YEARS
This Robotics and Machine Learning (ML) course offers an integrated approach to understanding the fundamentals of robotics combined with the power of machine learning algorithms to create intelligent, autonomous systems. The curriculum starts with an introduction to robotics, covering mechanics, control theory, and sensor integration, before delving into how machine learning can be applied to enhance the autonomy and decision-making capabilities of robots. Through practical examples and hands-on projects, students will explore the application of supervised, unsupervised, and reinforcement learning techniques in robotic perception, navigation, and manipulation tasks. The course emphasizes the development of algorithms that enable robots to learn from and adapt to their environment, making them more efficient and versatile in performing complex tasks.
In addition to technical skills, the course also focuses on the ethical implications and societal impacts of deploying autonomous robotic systems, ensuring students are prepared to tackle both the technical challenges and ethical considerations in their future careers. Advanced topics include deep learning applications in robotics, such as convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for processing sequential data, which are critical for tasks like autonomous navigation and human-robot interaction. By the end of the course, students will have a thorough understanding of how machine learning algorithms can be applied to robotics, equipping them with the skills to design, implement, and innovate at the intersection of these two transformative technologies.
In addition to technical skills, the course also focuses on the ethical implications and societal impacts of deploying autonomous robotic systems, ensuring students are prepared to tackle both the technical challenges and ethical considerations in their future careers. Advanced topics include deep learning applications in robotics, such as convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for processing sequential data, which are critical for tasks like autonomous navigation and human-robot interaction. By the end of the course, students will have a thorough understanding of how machine learning algorithms can be applied to robotics, equipping them with the skills to design, implement, and innovate at the intersection of these two transformative technologies.
Eligibility:
The candidates should have secured a minimum of 45% marks in aggregates (40% marks in case of SC/ST candidates) in three subjects with Physics, Mathematics as Compulsory Subject and any one of Chemistry / Biology / Bio-Chemistry / Computer Science / Electronics and shall have completed PUC 10+2 or its equivalent with English as one of the Subject.