This advanced course bridged the gap between academic robotics and production-floor reality. I mastered the complete automation stack: from low-level discrete I/O interfacing and PLC ladder logic to high-level MES integration and cloud-based analytics. Key achievements included programming 6-axis FANUC robots with vision guidance and force control, designing PLC programs with Ethernet messaging for controller-to-controller communication, and implementing OPC UA servers to enable data exchange between robots, PLCs, and enterprise systems.  Earning SACA certifications proved I can not only build these systems but troubleshoot and maintain them under industrial conditions.

This course provided an introduction to the Industrial Internet of Things (IIoT), covering its core technologies, architectures, communication protocols, and practical applications in manufacturing. It examined the role of IIoT in enabling data-driven manufacturing, with an emphasis on factory and shop floor data collection. Key industry standards—such as OPC UA, MTConnect, MQTT, SECS/GEM, and ISA-95 —were explored in the context of their integration into smart and digital manufacturing systems.

This course taught me to think like a systems engineer—seeing the big picture while managing intricate details. Through hands-on project involving an autonomous robot car, I learned the complete lifecycle of systems engineering: from architecture design and component integration to validation and deployment. The project brought together actuators, sensors, localization algorithms, and motion planning into a functioning autonomous system, teaching me that successful engineering isn't just about making parts work—it's about making them work together seamlessly.

This course bridged the gap between robotics theory and industrial reality. Learning IEC 61131-3 Ladder Logic programming and virtual PLC development, I discovered how factories actually work—from sensor selection and motion control to deploying complete automation solutions. Working with industry-standard simulation tools, I designed and tested control algorithms for real-world scenarios, learning that smart manufacturing isn't just about automating tasks, but about understanding the entire production ecosystem and designing systems that are robust, scalable, and cost-effective.

This course taught me how to integrate the mechanical, electrical, and software systems. Designing control systems for DC motors and thermal systems in Simulink, I learned to tune P, PD, PI, and PID controllers by understanding system dynamics. I learned that mechatronics is about more than just connecting components—it's about understanding how sensors, actuators, and microcontrollers communicate to create intelligent behavior. System identification and control tuning showed me that the best designs emerge from understanding the physics first, then building the control architecture around it.

This course gave me the tools to turn data into insight and insight into action. From building shallow neural networks to implementing transformers, VAEs, and diffusion models in PyTorch, the course challenged me to think critically about model selection, regularization, and performance measurement while applying these techniques to real mechanical engineering problems like Aero-foil design, forecasting electricity consumption. The key lesson wasn't just how to train models—it was understanding how to measure what matters, and why some architectures work better than others for specific engineering problems.

This course took me from understanding drone physics to making them fly autonomously. Using the Parrot Mambo drone and MATLAB/Simulink's hardware support package, I designed control architectures that went directly from simulation to real hardware—watching theory become flight in real-time. From deriving quadcopter dynamics and tuning attitude controllers to exploring fixed-wing aircraft control, I learned that autonomous flight is a delicate balance of physics, mathematics, and robust control design.

This course showed me that manufacturing is evolving from subtractive to additive, from mass production to mass customization. Exploring everything from conventional metal forming to laser-based additive manufacturing, stereolithography, and FDM, I learned how material science and process selection fundamentally shape what's possible to build. Understanding the capabilities and limitations of each process—why FDM works for rapid prototyping but laser sintering for functional parts, or how material properties dictate process choice—taught me that good design isn't just about what you create, but how you make it manufacturable.

This course was where the math behind robotics finally clicked. Diving deep into rotation matrices, homogeneous transforms, and Jacobian analysis, I learned that every robot motion is fundamentally a linear algebra problem solved in 3D space. From deriving forward and inverse kinematics to understanding robot dynamics and control, the theory revealed why robots move the way they do—and how to make them move better. Working through the mathematics of robotic arms taught me that elegant control starts with rigorous modeling.

This course taught me to see control systems through state-space glasses—a more powerful lens than classical control methods. From modeling complex physical systems to designing state feedback controllers and observers in MATLAB, I learned that modern control theory is about understanding what's happening inside a system, not just at its output. Exploring controllability, observability, and optimal control revealed that good control design isn't just about making systems stable—it's about making them optimally responsive while respecting physical constraints.

This course demystified how engineering simulation software actually works—by making me build it from scratch. Starting from governing PDEs for heat transfer and stress analysis, I learned to derive weak forms, construct shape functions, and assemble global systems of equations in MATLAB. From 1D bar elements to 2D/3D stress and thermal analysis, writing my own FEA code taught me something commercial software never could: what's happening under the hood when you click "solve." Understanding Gaussian quadrature, shape functions, and verification methods transformed me from an FEA user into someone who knows when to trust the results—and when to question them.

This course revealed that linear algebra isn't just abstract mathematics—it's the language every engineering algorithm speaks. From solving linear systems and computing eigenvalues to understanding SVD and matrix factorizations, I learned that concepts like rank, orthogonality, and the four fundamental subspaces aren't just theory—they're the foundation of everything from control systems to machine learning. Implementing these methods programmatically for applications like data compression and image processing showed me why linear algebra is the silent workhorse behind modern engineering: it turns complex problems into matrix equations we can actually solve.