The advanced operating system course examines the structural aspects of operating system and how these provide support for general purpose, embedded, and real-time operating environments.

The machine learning course examines the field of machine learning with a focus on how to construct computer programs that automatically improve with experience.

The human computer interaction course provides key approaches to the design, development, and evaluation of human-computer interfaces, with an emphasis on usability, interaction paradigms, computer-mediated human activities, and implications to society.

The communication in automation course examines modern communication topology and architecture employed in industrial automation systems. Topics include communication components, principles of communication in industrial automation, architecture and topology of network communication, communication protocols as used in industrial automation in wired and wireless communication such as TCP/IP, RS-232, RS-485, Fieldbus, DNP3.0, Modbus, Zigbee, Bluetooth, and IDRA, error detection and control, troubleshooting, and introduction to security in industrial automation systems.

The robotic system course focuses on the design and programming of robotic systems and issues in current research in robotics.

The programming for industrial automation course focuses on the programming mechanisms for modern devices in the control operations in industry. Topics include functional block diagrams, control system software for PLC and SCADA, programming techniques for PLC, computer based controllers, microcontroller, digital computer interface including isolated and non-isolated digital I/O, ADC, DAC, and engineering applications in selected industry in Ghana

The control systems design course examines the basic issues and theoretical foundation in the analysis and design of computer control systems for industrial applications.

The industrial controls course present an overview of advanced controller design strategies for multivariable industrial processes, starting from PID control structure to the more advanced H-infinity design technique.

The Applied optimization methods course examines the optimization theory and techniques for practical engineering problems. Topics include classification of optimization problems, linear programming, nonlinear unconstrained optimization, nonlinear constrained optimization including inequality and equality constraints, Lagrange multiplier method, and Kuhn-Tucker conditions and solution methods, dynamic programming, random search algorithms and search methods for optimization, convex optimization methods, and application examples from different engineering disciplines.

The adaptive signal processing course provides introduction to relevant signal processing and basics of pattern recognition.