DESCRIPTION AND CONTENT OF EACH COURSE

Robotics:

The goal of this course is to teach you the fundamentals of robot modeling, design, planning, and control. The information covered in this course is essentially a quick overview of relevant results from geometry, kinematics, statics, dynamics, and control.

The course follows a normal lecture, reading, and problem-solving approach. A midterm and final examination project will be required. Lectures will be based on content from the Lecture Notes book, but not solely. The lectures will be generally in the same order as the information provided in the book, so you can read it ahead of time.

Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming, and design.

Coding:

Deep learning is the machine learning approach that underpins some of the most interesting skills in fields as diverse as robotics, natural language processing, picture identification, and artificial intelligence, including the well-known AlphaGo. In this course, you'll learn how to use Keras 2.0, the most recent version of a cutting-edge deep learning toolkit in Python, to do actual deep learning tasks.

You'll discover the core concepts and vocabulary of deep learning, as well as why deep learning approaches are so effective today. You'll create rudimentary neural networks and use them to make predictions. Learn how to make your neural network predictions more accurate. Backward propagation is one of the most significant strategies in deep learning, and you'll apply it. Understanding how it works will provide you with a solid foundation upon which you may build in the second part of the course.

The Keras library will be used to create deep learning models for regression and classification. You'll learn how to construct predictions using the Specify-Compile-Fit approach, and by the conclusion of the chapter, you'll have all the tools you need to build deep neural networks. Learn how to use Keras to enhance your deep learning models. Begin by understanding how to validate your models, then learn about model capacity, and lastly, experiment with larger and deeper networks.

Aerospace:

Aeronautics, astronautics, and design lectures address the essential principles and methodologies of aerospace engineering. Information technology is used in active learning aeronautical courses. Student groups participate in a hands-on lighter-than-air (LTA) vehicle design project, designing, building, and flying radio-controlled LTA vehicles. In the design activities, the linkages between theory and practice are realized.

The LTA race competition is preceded by required design assessments. The performance, weight, and primary characteristics of the LTA vehicles are estimated and presented using freshman-level physics, maths, and chemistry, with an emphasis on applying this knowledge to aeronautical engineering and design rather than on novel science and mathematics.

CERTIFICATIONS