Building AI Systems: From First Principles
Program Highlights
- Duration: 8 weeks
- Total learning time: 60 live contact hours
- Sessions: 40 live sessions
- Schedule: Monday to Friday, 1.5 hours per day
- Mode: Live online
- Final project: Two-week end-to-end AI system design capstone
An 8-Week Live Online Program for Students and Early-Career Engineers
Learn to think beyond AI models and understand how complete AI systems are designed, evaluated, and prepared for real-world use.
Artificial intelligence is more than choosing a model or using an AI tool. Building a useful AI system requires understanding the problem, identifying the right data, selecting an appropriate architecture, evaluating performance, anticipating failures, and planning for deployment.
Building AI Systems: From First Principles is an eight-week, design-focused online program that helps learners develop these skills through conceptual learning, system-design exercises, weekly assignments, review sessions, and a practical capstone project.
Rather than focusing only on individual AI tools or ready-made models, the program emphasises the reasoning and engineering decisions behind building complete AI systems
Note: Session timings and registration details will be announced separately.
About the Program
From Understanding AI to Designing AI Systems
Many people learn to use AI applications or experiment with machine learning models. However, designing an AI system for a real-world problem involves a much broader set of decisions.
This program introduces learners to the complete thinking process behind AI system development.
Participants will explore how AI architectures have evolved, how to frame complex problems as machine-solvable tasks, how to plan data collection when suitable datasets are unavailable, and how to select and evaluate system architectures under practical constraints.
The emphasis is on understanding why particular design decisions are made, not simply which tools to use.
What Will You Learn?
AI Foundations and System Architecture
Understand how AI systems work beyond individual models. Explore the evolution of AI, from symbolic systems and early neural networks to modern attention mechanisms and Transformers. Learn to examine an AI product by breaking it down into its inputs, decisions, feedback loops, and learning processes.
Mathematical and Conceptual Foundations
Develop an intuitive understanding of optimisation, loss landscapes, probability, uncertainty, bias–variance trade-offs, overfitting, generalisation, and feature learning. Focus on understanding these concepts and their implications for system design.
Multimodal AI and Problem Framing
Explore how AI systems process text, images, audio, and time-series data. Learn to translate real-world challenges into machine-learning tasks, break problems into manageable components, and choose suitable learning approaches and evaluation metrics.
Data-Centric AI
Learn why data quality and data strategy are critical. Topics include data collection, labelling, annotation bias, active learning, weak supervision, synthetic data, and the challenges of transferring simulated results into real-world environments. Develop a data acquisition and labelling plan for a problem that may not have an existing dataset.
Model Selection, Training and Evaluation
Understand decisions about building, buying, or fine-tuning models. Explore open and proprietary models, training strategies, computing costs, evaluation methods, model debugging, error analysis, bias, fairness, and safety. Practise proposing an architecture within a defined budget and identifying potential failure modes.
Deployment and AI Lifecycle Management
Explore inference latency, operational costs, monitoring, drift detection, retraining strategies, feedback loops, and regulatory and ethical readiness. Understand how AI systems can be monitored, maintained, evaluated, and improved over time.
Capstone: Design a Complete AI System
During the final two weeks, apply the concepts to design an AI system for a real-world problem of your choice. Work through problem definition, data acquisition, architecture selection, training strategy, evaluation planning, failure analysis, and an iteration and scaling roadmap. The project includes review checkpoints, a final report, and a presentation.
Eight-Week Learning Roadmap
Week 1 — Foundations of AI Thinking & Architecture Evolution
Understand AI systems, examine why AI projects fail, and explore the evolution of different AI architectures.
Week 2 — Mathematical & Conceptual Core
Study optimisation, uncertainty, bias and variance, overfitting, generalisation, and interpretability.
Week 3 — Modalities, Multimodal Systems & Problem Framing
Explore language, vision, audio, time-series data, multimodal systems, and real-world problem decomposition.
Week 4 — Data-Centric AI
Develop strategies for collecting, generating, labelling, and improving data when suitable datasets are unavailable.
Week 5 — Model Strategy, Training & Evaluation
Learn to select models, manage computing and budget constraints, evaluate results, debug errors, and identify risks.
Week 6 — Deployment & Lifecycle Thinking
Understand inference costs, monitoring, data and model drift, retraining, and responsible AI deployment.
Week 7 — Capstone: Design the System
Define your problem, develop a data plan, select an architecture, and establish training and evaluation strategies.
Week 8 — Capstone: Stress-Test & Present
Identify potential failures, develop mitigation strategies, prepare the final report, and present your AI system design.
How Is the Program Conducted?
- Live sessions: Instructor-led sessions from Monday to Friday.
- System-design discussions: Work through the reasoning behind AI architectures and design choices.
- Weekly assignments: Apply concepts through problem analysis, architecture proposals, and data planning.
- Review checkpoints: Structured reviews, including milestone evaluations at the end of Weeks 3 and 6.
- Learning resources: Session recordings, learning resources, and design templates shared throughout the program.
- Capstone mentoring: Guidance and feedback at key stages of the final project.
Expected Learning Outcomes
By the end of the program, learners are expected to be able to:
- Convert ambiguous real-world problems into clearly defined AI tasks.
- Plan data collection, data preparation, and labelling strategies.
- Select and justify AI architectures based on practical constraints.
- Develop evaluation plans that go beyond standard benchmark metrics.
- Identify potential AI system failures and plan mitigation strategies.
- Design AI systems with deployment, monitoring, and future iteration in mind.
- Present a structured AI system design supported by a final capstone report.
Who Should Attend?
This program is designed for anyone interested in understanding how AI systems are designed and how artificial intelligence can be applied to real-world problems.
It is suitable for:- First-year and other undergraduate students interested in exploring AI.
- Final-year students and recent graduates from engineering, science, and related disciplines.
- Master’s students interested in AI, Machine Learning, or Data Science.
- Working professionals looking to transition into AI-related careers.
- Software engineers and professionals from other fields who want to expand their knowledge of AI.
- Aspiring entrepreneurs and innovators interested in developing AI-driven products.
Prerequisites
No advanced AI or Machine Learning knowledge is required to join this program.
Participants should have:- Basic familiarity with computers and an interest in learning about AI.
- A willingness to explore technical concepts and solve problems.
- A laptop and a stable internet connection to attend live online sessions.
Ready to Understand How AI Systems Are Built?
Move beyond using AI tools and start understanding the decisions behind complete AI systems.
Join Building AI Systems: From First Principles, an eight-week live online program focused on AI problem-solving, architecture, data strategy, evaluation, and system design.