COMPSCI 713 — AI Fundamentals: Exam Preparation
University of Auckland | Semester 1, 2026 | Instructor: Xinyu Zhang
About This Book
This knowledge base is built to help you learn and prepare for the COMPSCI 713 in-course test (Week 7, 60 minutes, 20 marks).
Every concept is explained using the Feynman method: first in plain language with analogies, then formally with math, then applied to real exam questions. The goal is not just memorisation — it’s understanding.
How to Use This Book
- Start with Part 0 — read the exam analysis to understand what’s tested and with what weight
- Work through modules in priority order — 🔴 modules first (A, B, G, F), then 🟠 (D, H), then 🟡 (C, E)
- For each chapter: read the Feynman Draft first to build intuition, then study the formal definitions, then try the practice problems
- Use the English Expression Guide before the test — practise the sentence templates
- Attempt all 3 mock exams under timed conditions (55 min answering)
- Check your cheat sheet — the frequency map chapter has recommendations for what to write on your handwritten A4 page
Exam Format (Sample Test S1 2026)
| Item | Detail |
|---|---|
| Duration | 60 min (5 min reading + 55 min answering) |
| Total marks | 20 |
| Questions | 6 short-answer questions |
| Notes allowed | Double-sided handwritten A4 page |
| Calculators | Not permitted |
| Style | Quality over quantity — concise, clear answers |
Coverage Map (Weeks 2-5)
| Week | Lecture | Topic | Module |
|---|---|---|---|
| W2 | L1 | Symbolic Logic (Propositional + FOL) | A 🔴 |
| W2 | L2 | Logic Neural Networks (LNN) | B 🔴 |
| W3 | L1 | Knowledge Representation (Expert Systems, Ontologies, KG) | C 🟡 |
| W3 | L2 | Knowledge Graphs for AI (TransE, Embeddings, RAG) | D 🟠 |
| W4 | L1 | MYCIN Expert System (Confidence Factors) | E 🟡 |
| W4 | L2 | Decision Trees & Ensembles (Bagging, Boosting) | F 🔴 |
| W5 | L1 | Soft Computing (Fuzzy Logic, Bayesian, Vagueness vs Uncertainty) | G 🔴 |
| — | — | Multi-Agent Systems (Robot Soccer) | H 🟠 |
Priority Legend
- 🔴 必考 (Must-Know): Appeared in sample test with high mark weight
- 🟠 高频 (High Frequency): Appeared in sample test with moderate weight
- 🟡 中频 (Medium): Full lecture topic, not in sample but likely in actual test