← MindForgeAI homeJanuary 2027 proposed cohort · Admissions not openConnected campus
LaunchpointBY MINDFORGEAI
← Course catalogue

MACHINE LEARNING / SPECIALIST

AI210Classical AI: search, reasoning & planning

24 h productive allocation9 h 36 m learning + 14 h 24 m training4 units · 1 assessed practical brief

Detailed course plan and training brief. A complete taught course pack is in preparation. The linked foundation module, where shown, is a smaller available resource.

BEFORE YOU BEGIN

A clear starting point.

Prerequisites: AI101 · MTH110

Specialist studio; environment and faculty must be confirmed before scheduling

WHAT YOU SHOULD BE ABLE TO DEMONSTRATE

  1. Explain the assumptions and failure modes in search and heuristics.
  2. Complete the practical: implement a small route-planning or constraint-solving problem and compare two search strategies using explicit costs.
  3. Defend the resulting problem specification with reproducible evidence.

THE SUBJECTS / FOUR CONNECTED UNITS

What you will study.

UNIT 016 h allocated

Search and heuristics

  • State spaces
  • breadth-first and uniform-cost search
  • A-star
  • admissibility
UNIT 026 h allocated

Knowledge representation

  • Propositional logic
  • rules
  • ontologies
  • inference
UNIT 036 h allocated

Planning and constraints

  • Goals and actions
  • constraint satisfaction
  • scheduling
  • search costs
UNIT 046 h allocated

AI-system design

  • Symbolic versus learned components
  • uncertainty
  • hybrid systems
  • task-based evaluation

THE TRAINING STUDIO

Make the subject
visible in your work.

Implement a small route-planning or constraint-solving problem and compare two search strategies using explicit costs.

Evidence to produce

  • Problem specification
  • search trace
  • correctness tests
  • complexity comparison

Training brief for supervised delivery. Code, datasets, answer keys and environment acceptance must be completed in the full course pack before delivery is confirmed.

ASSESS THE REASONING

A question you
should be able to answer.

Why is a learned predictor not a substitute for every search or reasoning task?

Review dimensions

Technical correctness · reproducibility · interpretation of results · failure analysis · individual explanation. Course-level rubric and faculty moderation form part of the final adopted teaching pack.

External reference access, hardware, cloud accounts and paid services are separate. No third-party certification is included by implication.

Search by topic or purpose. Press Escape to close.