An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The Pre-Master's in Artificial Intelligence at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| FND011 | Foundations of Artificial Intelligence & Algorithmic Problem Solving | Theory | Develop computational thinking, structured problem decomposition and algorithmic problem solving in Artificial Intelligence. |
| FND012 | Structured Programming for Artificial Intelligence with Python | Practical | Write, debug and test clean Python scripts, functions, loops and data structures for Artificial Intelligence applications. |
| FND013 | Mathematics & Quantitative Foundations for Artificial Intelligence | Theory | Solve linear equations, functions, coordinate geometry and basic probability for computing applications in Artificial Intelligence. |
| FND014 | Computer Systems Architecture & Terminal Basics in Artificial Intelligence | Practical | Navigate Linux file systems, manage system processes and write command-line scripts in Bash for Artificial Intelligence. |
| FND015 | Academic English & Technical Communication in Artificial Intelligence | Theory | Develop academic writing, technical reporting, presentation skills and literature research habits in Artificial Intelligence. |
| FND016 | Interactive Web Foundations & Digital Interfaces for Artificial Intelligence | Practical | Create structured web pages using HTML5, CSS3 styling and JavaScript interactivity for Artificial Intelligence interfaces. |
| FND017 | Data Representation & Information Structures in Artificial Intelligence | Theory | Analyze binary representation of numbers, character encodings (ASCII/UTF-8) and data modeling in Artificial Intelligence. |
| FND018 | Relational Databases & SQL Essentials Lab for Artificial Intelligence | Practical | Create relational database tables, insert records and write structured SQL SELECT queries with joins for Artificial Intelligence. |
| FND019 | Digital Ethics, Security Awareness & Independent Study in Artificial Intelligence | Theory | Analyze cyber hygiene principles, intellectual property rules and ethical online academic practices in Artificial Intelligence. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| FND021 | Object-Oriented Design & Problem Solving in Artificial Intelligence | Theory | Model software concepts using object-oriented principles, classes, objects, encapsulation and inheritance in Artificial Intelligence. |
| FND022 | Practical Coding Workshop & Lab in Artificial Intelligence | Practical | Build multi-module programs in Python or Java incorporating modular design patterns and unit tests for Artificial Intelligence. |
| FND023 | Discrete Mathematics, Logic & Set Theory Foundations for Artificial Intelligence | Theory | Apply truth tables, propositional logic statements, sets and basic graph representations to Artificial Intelligence. |
| FND024 | Software Engineering Tools, Git & Version Control in Artificial Intelligence | Practical | Initialize Git repositories, create branches, resolve merge conflicts and collaborate via GitHub in Artificial Intelligence. |
| FND025 | Computer Networks, Web Protocols & Internet Systems in Artificial Intelligence | Theory | Analyze IP addressing, DNS resolution, HTTP request-response cycles and network routers for Artificial Intelligence. |
| FND026 | Data Analysis, Spreadsheets & Introductory Pandas Lab for Artificial Intelligence | Practical | Clean tabular data, compute summary statistics and generate charts using Excel and Python Pandas in Artificial Intelligence. |
| FND027 | Operating Systems Fundamentals & System Administration in Artificial Intelligence | Theory | Explain CPU process scheduling, memory allocation, file systems and operating system security for Artificial Intelligence. |
| FND028 | Practical Application Mini-Project in Artificial Intelligence | Practical | Develop a functional software application or analytical dashboard solving a realistic problem in Artificial Intelligence. |
| FND029 | Foundation Capstone Project & Technical Portfolio in Artificial Intelligence | Practical | Present the completed portfolio of coding artifacts and oral defense of foundational computing concepts in Artificial Intelligence. |
Secondary school completion or relevant initial technical curiosity. English proficiency recommended.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.