CS 423 explores the fundamentals of modern operating system design, from CPU and memory virtualization to multi-threading and filesystems. Students gain hands-on experience building custom Linux kernel modules, working with the proc filesystem, ioctl, and system calls. Along the way, you’ll dive into schedulers, inodes, OS-level security and process management.

CS 483 ventures deep into the world of modern parallel programming using NVIDIA GPUs and CUDA. Students combine concepts like threads, warps, and thread blocks with global and shared memory systems to deconstruct problems down into tens of thousands of parallel tasks that can then be processed with remarkable efficiency.

CS 411 teaches relational database system design from the ground up, building from basic SQL syntax to more advanced procedures, tirggers, transactions, relational diagrams, and query optimization.

CS 431 bridges the gap between hardware and software with a hands-on approach to embedded systems programming. Students program 2 wheel bipedal robots using concepts like real-time operating systems, proportional-integral-derivative (PID) control, and rate monotinic scheduling while touching on relevant tooling like cmake, make, and Vim.

CS 421 teaches the structure of programming languages, abstract data types, functional programming, lexing, parsing, semantic analysis and code generation. Students come away from this course with a solid understanding and appreciation for the complexities and mechanisms involved in building modern programming languages.

CS 473 — better known as Leetcode Extreme — is considered by many to be one of the hardest computer science courses offered at the University of Illinois. Advanced dynamic programming problems, proofs, flow networks, non-determinism and streaming algorithms are all fair game here, be prepared to go well beyond your comfort level and get ready to master the art of algorithm design.

CS 444 provides an elementary hands-on introduction to neural networks and deep learning with an emphasis on computer vision applications. Students build linear classifiers, multi-layer convolutional neural networks, generative adversarial networks (GANs), variational auto-encoders, and transformer models using PyTorch.