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Documentation Index

Complete guide to all documentation in this repository.

Core Documentation

README.md

Main repository overview and entry point.

Contents:

  • Repository overview and mission
  • Learning path diagram
  • Complete lesson structure (Basic, Intermediate, Advanced, Expert)
  • Getting started guide
  • Usage and learning tips
  • Progress tracker for all 60 lessons
  • Community and support information

Best for: First-time visitors, understanding repository structure, tracking overall progress

View README.md

MAI Student Guide

Comprehensive guide specifically for University of Auckland MAI students.

Contents:

  • Direct course mappings (COMPSCI 713, 714, 761, 762, 703, COMPSYS 721)
  • Semester-by-semester study strategies
  • Assignment preparation tips and templates
  • Research project ideas for dissertations
  • Exam preparation strategies
  • Study group formation tips
  • Career preparation advice

Best for: UoA MAI students, course alignment, semester planning

View MAI Student Guide

Exam Preparation Guide

Comprehensive exam preparation strategies and study tips.

Contents:

  • Course-specific guidance for all UoA COMPSCI courses
  • Study strategies for different question types
  • Exam question examples and patterns
  • Tips for success before, during, and after exams
  • Quick reference: lesson to course mapping
  • Practice problem approaches

Best for: Exam preparation, concept review, understanding exam expectations

View Exam Preparation Guide

Progress Tracker Guide

How to effectively track your learning progress.

Contents:

  • Overview of progress tracking features
  • How to use notebook trackers
  • Spaced repetition schedule
  • Example workflows
  • Tips for effective tracking
  • Integration with README tracker

Best for: Organizing study sessions, tracking completion, planning reviews

View Progress Tracker Guide

Updates Summary

Complete overview of recent repository enhancements.

Contents:

  • Detailed list of all updates
  • Progress tracker additions
  • Exam preparation sections
  • New documentation files
  • Usage guide for new features
  • Statistics and coverage

Best for: Understanding new features, seeing what's been added, learning about enhancements

View Updates Summary

Advanced Lessons Complete

Documentation of the completion of all 15 Advanced level lessons.

Contents:

  • Complete list of all Advanced lessons (A01-A15)
  • Lesson structure and organization
  • UoA MAI course alignment
  • Next steps for content development
  • Technical details and verification
  • Repository status overview

Best for: Understanding Advanced level completion, tracking curriculum progress

View Advanced Lessons Complete

COMPSCI 714 Complete Guide

Unified guide for COMPSCI 714 - AI Architecture and Design course.

Contents:

  • Complete topic coverage for Lectures 2-3
  • Cross-references between lectures
  • Unified learning path for semester
  • Quick reference formulas and algorithms
  • Comprehensive exam preparation checklist
  • Links to detailed lecture guides
  • Repository resource mapping

Best for: COMPSCI 714 students, course overview, semester planning, exam review

View COMPSCI 714 Complete Guide

COMPSCI 714 Lecture 2: Neural Networks

Detailed guide for Lecture 2 - Neural Networks fundamentals.

Contents:

  • Artificial neuron anatomy (inputs, weights, bias, pre-activation, activation)
  • Linear algebra representation (y = g(wᵀx + b))
  • The Perceptron (Rosenblatt, 1958) and historical context
  • Multilayer Perceptron (MLP) architecture
  • Deep Neural Networks (DNN) formal definition
  • Hierarchical feature learning intuition
  • Universal Function Approximation Theorem (formal and intuitive)
  • Gradient descent and steepest descent mathematics
  • Repository mapping and practical guide

Best for: Lecture 2 preparation, theoretical understanding, exam prep

View Lecture 2 Guide

COMPSCI 714 Lecture 3: Training & Optimization

Detailed guide for Lecture 3 - Training and optimization.

Contents:

  • Learning as optimization problem
  • Gradient descent algorithm (w ← w - α∇L(w))
  • Learning rate selection and effects
  • Gradient descent variants (Batch, SGD, Mini-batch)
  • Backpropagation algorithm and chain rule
  • Worked backpropagation example (Matt Mazur)
  • Automatic differentiation tools (TensorFlow, PyTorch)
  • Computational complexity analysis
  • Repository mapping and practical guide

Best for: Lecture 3 preparation, understanding optimization, backpropagation mastery

View Lecture 3 Guide

Policy Documentation

Academic Integrity Policy

Guidelines for responsible use of this repository.

Contents:

  • Appropriate and inappropriate use
  • Guidelines for students
  • Citation requirements
  • UoA-specific policies
  • Consequences of academic misconduct

Best for: Understanding ethical use, citation guidelines, academic honesty

View Academic Integrity Policy

Contributing Guidelines

How to contribute to this project.

Contents:

  • Contribution process
  • Code standards
  • Documentation requirements
  • Pull request guidelines

Best for: Contributors, improving documentation, adding content

View Contributing Guidelines

Security Policy

Security guidelines and vulnerability reporting.

Contents:

  • Security policies
  • Vulnerability reporting process
  • Supported versions

Best for: Security researchers, reporting issues

View Security Policy

Lesson Documentation

Each lesson notebook (B01-B15, I01-I15, E01-E15) includes:

Key Takeaways Section

  • 5 essential concepts
  • Mathematical formulations
  • Practical understanding
  • Exam-focused summaries

Exam Preparation Guide

  • Essential concepts for exams
  • Common mistakes to avoid
  • Practice problems
  • Course mapping
  • Study tips
  • Exam question types

Progress Tracker

  • Completion status checklist
  • Date tracking (completed, reviewed, next review)
  • Understanding level rating (1-5)
  • Notes and reflections
  • Key concepts checklist

Quick Navigation

I'm a new visitor

Start with README.md to understand the repository structure and learning paths.

I'm a UoA MAI student

Read the MAI Student Guide for course-specific guidance and semester planning.

I'm preparing for exams

Check the Exam Preparation Guide for study strategies and practice problems.

I want to track my progress

See the Progress Tracker Guide for effective tracking methods.

I want to understand recent changes

Review the Updates Summary for all recent enhancements.

I want to contribute

Read the Contributing Guidelines for contribution process.

I need to cite this repository

Check the Academic Integrity Policy for citation guidelines.

Documentation Structure

AI/
├── README.md                          # Main entry point
├── MAI_STUDENT_GUIDE.md              # UoA student guide
├── EXAM_PREPARATION_GUIDE.md         # Exam strategies
├── PROGRESS_TRACKER_GUIDE.md         # Progress tracking
├── UPDATES_SUMMARY.md                # Recent changes
├── DOCUMENTATION_INDEX.md            # This file
├── ACADEMIC_INTEGRITY.md             # Ethics and citation
├── CONTRIBUTING.md                   # Contribution guide
├── SECURITY.md                       # Security policy
├── Basic/                            # 15 Basic lessons
│   ├── B01 - Arithmetic.ipynb
│   ├── B02 - Linear Regression.ipynb
│   └── ...
├── Intermediate/                     # 15 Intermediate lessons
│   ├── I01 - Advanced Optimization.ipynb
│   └── ...
├── Advanced/                         # 15 Advanced lessons (planned)
└── Expert/                           # 15 Expert lessons
    ├── E01 - Reading Papers.ipynb
    └── ...

Cross-References

All documentation files include cross-references to related documents at the end:

  • README.md links to all guides in Documentation Overview section
  • Each guide links to other relevant guides in Related Documentation section
  • Lesson notebooks reference guides for detailed information

Maintenance

This documentation is actively maintained and updated as the repository evolves.

Last Updated: March 1, 2026

Feedback

If you have suggestions for improving documentation:

  • Open an issue on GitHub
  • Submit a pull request
  • Connect on LinkedIn

Back to README