The academic content and curriculum of the Autonomous Systems Technician associate degree program aims to provide students with both theoretical knowledge and practical skills. The program includes various courses from engineering and technology disciplines. Below is a detailed breakdown of these courses.
Electrical and Electronics Engineering courses provide students with a basic understanding of electrical circuits, electronic components and systems. Topics such as Ohm's law, Kirchhoff's law, AC and DC circuit analysis, semiconductors and transistors are covered in these courses. Advanced topics such as digital electronics, microcontrollers and signal processing are also included in the curriculum.
Computer Programming courses provide students with software development skills. In these courses, programming languages such as C, C++, Python and MATLAB are taught. Topics such as algorithm development, data structures, software engineering principles and database management are also covered. Students gain practical experience through programming projects and assignments.
Control Systems courses are essential to understand the basic operating principles of autonomous systems. Topics such as feedback control theory, PID controllers, system dynamics and modeling are covered in these courses. Using this knowledge, students learn the design and optimization of robotic systems and automation applications.
Sensor Technologies courses cover the working principles and applications of sensors that enable autonomous systems to interact with their environment. In these courses, various types of sensors such as optical sensors, ultrasonic sensors, GPS, lidar and radar are taught. Processing and integration of sensor data is also an important part of this course.
Artificial Intelligence and Machine Learning courses provide students with the knowledge and techniques required for the development of intelligent systems. Topics include artificial neural networks, deep learning, supervised and unsupervised learning algorithms, data mining and big data analytics. Students gain practical experience through machine learning projects.
Mathematics and Physics courses form the basis of the technical courses. Mathematics courses cover topics such as calculus, linear algebra, differential equations and probability theory. Physics courses cover basic physics principles such as mechanics, electromagnetism, thermodynamics and wave physics.
Laboratory and Project Studies are critical for students to reinforce their theoretical knowledge in practice. While students model and analyze real-world systems in laboratory courses, they develop their own designs and solutions in project work. These projects often require teamwork and provide students with problem-solving and project management skills.
The curriculum of the Autonomous Systems Technician associate degree program provides students with a broad range of knowledge, preparing them to work in a variety of industries. The program offers students a comprehensive education through both theoretical courses and practical applications. This provides graduates with the knowledge and skills necessary for a successful career in the rapidly developing field of autonomous systems.