Artificial Intelligence Foundations

Course Description

Artificial Intelligence Foundations introduces the core problem-solving paradigms of AI: search, knowledge representation, and an introduction to learning-based approaches, always with attention to what a technique can and cannot reasonably claim to do.

Learning Outcomes

Formulate a problem as a search or constraint-satisfaction task.
Implement classic search algorithms and analyze their behavior.
Explain the basic principles behind learning-based approaches to AI.
Critically assess claims made about an AI system's capabilities.

Weekly Topics

Examines what ai is (and isn't) from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend problem formulation & search, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on uninformed search with an in-class exercise students carry into the week's assignment.

Focuses on common mistakes and misconceptions around informed search & heuristics, using student work from the previous assignment as material.

Introduces constraint satisfaction and immediately puts it to use in a small design or implementation task.

Situates adversarial search within the broader arc of the course, showing how it connects to what comes next.

Uses a guest dataset or scenario to explore mid-semester review in a setting closer to professional practice.

Introduces knowledge representation through short lectures and worked examples, building the vocabulary the rest of the course relies on.

Works through reasoning under uncertainty in a lab-driven session, with guided exercises students complete and discuss in small groups.

Covers introduction to machine learning in depth, connecting the underlying theory to a concrete example the class builds together.

Examines introduction to neural networks from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend ai ethics & limitations, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on revision with an in-class exercise students carry into the week's assignment.

Assessment

Assignments — 20%

Practical Work — 25%

Mid-Semester — 20%

Final Examination — 35%

Prerequisite Map

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