Foundations for AI

Foundations for AI is an intensive, ground-up computer science education for people who want to build AI systems rather than just use them. It begins with binary representation and computer architecture and ends with containerized applications running across multiple nodes in the cloud. Covering the full stack of knowledge that modern AI infrastructure is built on in its entirety.

The course moves through six connected modules. You start with computing fundamentals and the Python language, then learn the data structures and algorithms that underpin efficient software. From there you move into the Linux command line and the tooling professional engineers work in daily. The networking and web applications module takes you from how an HTTP request travels across the internet to deploying a secure, database-backed web app of your own. Advanced Python introduces the mathematical and numerical computing foundations like calculus, NumPy, and Matplotlib, that machine learning depends on. The final unit goes deep into systems and cloud: processors and GPUs, the kernel, concurrency, memory management, virtualization, Docker, and AWS deployment.

Two capstone projects anchor the work. You will build and deploy a full web application, and complete a second project applying Python’s numerical stack to a substantial problem. Throughout, the emphasis is on doing the work rather than watching trying to memorize concepts. You’ll write code, break things, read error messages, fix problems and develop real projects perfect for your portfolio of work.

By the end of this course, students will be able to:

1. Develop programming fluency in Python from basic syntax and control flow through object-oriented design, decorators, and testing practices used in production codebases.
2. Build a working mental model of how computers actually operate: binary representation, processor architecture, memory hierarchy, the kernel, and how a line of code becomes instructions a CPU executes.
3. Gain command-line and systems administration competence in Linuxincluding filesystems, permissions, package management, remote access, logging, and version control with Git
4. Understand and implement core data structures and algorithms,with the ability to reason about tradeoffs in efficiency and choose appropriate approaches for a given problem.
5. Learn how the internet and web applications work end to end, from DNS and HTTP through frontend JavaScript, server deployment, databases, REST APIs, and TLS certificate management.
6. Acquire the mathematical and numerical computing foundation for machine learning,covering the calculus needed for advanced Python work and practical fluency in NumPy and Matplotlib.
7. Master modern deployment infrastructure,including concurrency, virtualization, containerization with Docker, cloud computing models, AWS services, access management, load balancing, and monitoring.
8. Establish professional engineering habits testing with pytest, linting, dependency management with uv, and version control workflows that transfer directly to team environments.

Upon successful completion of this course, students will be able to:

Computing Fundamentals

• Explain how data is represented in binary and how computer architecture executes program instructions
• Apply logical reasoning and control flow constructs to solve computational problems
• Write Python programs using variables, operators, functions, data structures, and file I/O

Software Engineering

• Design and implement object-oriented Python programs using classes, inheritance, and decorators
• Select and implement appropriate data structures and sorting or searching algorithms for a given problem, and justify the choice on efficiency grounds
• Write test suites with pytest and enforce code quality standards using pylint and black
• Manage projects and collaborate using Git version control and uv for dependency management

Linux and Systems Administration

• Navigate and manipulate a Linux filesystem from the command line
• Configure users, groups, and file permissions to control system access
• Install and manage software packages, configure networking, and administer remote systems
• Interpret system logs to diagnose operational problems

Networking and Web Development

• Describe how a request travels from browser to server and back, including DNS, HTTP, and internet architecture
• Build frontend interfaces using JavaScript and distinguish client-side from server-side responsibilities
• Deploy a web server, integrate a database, and expose functionality through REST APIs
• Configure SSL certificates with Let’s Encrypt and explain browser isolation as a security boundary
• Capstone: Design, build, and deploy a complete, secured web application

Numerical Computing

• Apply calculus concepts as they arise in numerical and scientific Python programming
• Perform array-based computation with NumPy and produce visualizations with Matplotlib
• Capstone: Complete a substantial project applying Python’s numerical and scientific stack

Systems and Cloud Infrastructure

• Explain processor and GPU design and the role of the kernel in managing hardware resources
• Analyze processes, threads, concurrency, mutual exclusion, scheduling, memory management, and inter-process communication
• Describe filesystem structure and use tools such as fsck to diagnose filesystem integrity
• Explain the role of C in systems programming and convert performance-critical Python to C
• Differentiate virtualization from containerization, and build, run, and containerize applications with Docker
• Deploy applications to AWS using appropriate cloud service models, with secure networking and access management configured
• Implement multi-node deployments using parallelization, queues, and load balancing, with monitoring and security in place
• Describe modern computer hardware and the considerations involved in assembling a system