2. ENGG*3130 Lectures

• 2.1. About the Lectures
  • 2.1.1. Building a static version of the notes
  • 2.1.2. Contributing to the course notes
    • 2.1.2.1. Sphinx
    • 2.1.2.2. Recipe
    • 2.1.2.3. Checklist
• 2.2. Lecture 1: Introduction
  • 2.2.1. Overview
  • 2.2.2. About the course
  • 2.2.3. Frequently Asked Questions
• 2.3. Lecture 2: Complexity science
  • 2.3.1. Overview
  • 2.3.2. Breakout Groups
  • 2.3.3. Web of Life & Different Types of Problems
  • 2.3.4. Barriers to Learning
  • 2.3.5. Complexity Science
  • 2.3.6. Complexity Engineering & Thinking
• 2.4. Lecture 3: Python 1
  • 2.4.1. Python Review Class Overview
  • 2.4.2. VPN Setup
  • 2.4.3. JupyterHub
  • 2.4.4. Python Concepts
• 2.5. Lecture 4: Python 2
  • 2.5.1. Floor Division and Modulus
  • 2.5.2. Boolean Expressions
  • 2.5.3. Conditional Execution
  • 2.5.4. Alternative Execution
  • 2.5.5. Chained Conditionals
  • 2.5.6. Nested Conditionals
  • 2.5.7. Recursion
  • 2.5.8. Infinite Recursion
  • 2.5.9. Keyboard Input
  • 2.5.10. Debugging
  • 2.5.11. Return Values
  • 2.5.12. Boolean Functions
  • 2.5.13. More Recursion
  • 2.5.14. Checking Types
  • 2.5.15. Reassignment
  • 2.5.16. Updating Variables
  • 2.5.17. The while Statement
  • 2.5.18. The break Statement
  • 2.5.19. Square Roots
• 2.6. Lecture 5: Python 3
  • 2.6.1. Overview
  • 2.6.2. Strings
  • 2.6.3. Traversal with a for loop
  • 2.6.4. Lists
  • 2.6.5. Dictionaries
• 2.7. Lecture 6: Python 4
  • 2.7.1. Overview
  • 2.7.2. Classes and Objects
  • 2.7.3. Classes and Functions
  • 2.7.4. Classes and Methods
  • 2.7.5. Inheritance
  • 2.7.6. Tuples
• 2.8. Lecture 7: Graphs
  • 2.8.1. Overview
  • 2.8.2. Definitions
  • 2.8.3. Paths, cycles, and related terms
  • 2.8.4. Trees and forests
  • 2.8.5. Case study: COVID-19 contact tracing
  • 2.8.6. NetworkX (Python)
    • 2.8.6.1. Imports and setup
    • 2.8.6.2. Set the color palette
    • 2.8.6.3. Directed graphs
    • 2.8.6.4. Undirected graph example
    • 2.8.6.5. Complete graphs
    • 2.8.6.6. Random graphs
  • 2.8.7. Breakout rooms activity
    • 2.8.7.1. Group A + C: Course prerequisite graph
    • 2.8.7.2. Group B + D: Task dependency graph
  • 2.8.8. Announcements
  • 2.8.9. Note
• 2.9. Lecture 8: Small world graphs
  • 2.9.1. Overview
  • 2.9.2. Stanley Milgram and the Small World Experiment
  • 2.9.3. Regular Graphs and Random Graphs
  • 2.9.4. Clustering
  • 2.9.5. Shortest Path Lengths
  • 2.9.6. Ring Lattice
  • 2.9.7. Watts–Strogatz Small World Graphs
  • 2.9.8. The Watts–Strogatz Experiment
  • 2.9.9. Generative Explanations
  • 2.9.10. Breadth-First Search
  • 2.9.11. Dijkstra’s Algorithm
  • 2.9.12. Summary
• 2.10. Lecture 9: Scale-free networks
  • 2.10.1. Overview
  • 2.10.2. Ego Networks
  • 2.10.3. SNAP Facebook Dataset
  • 2.10.4. Watts–Strogatz Model
  • 2.10.5. Degree
  • 2.10.6. Heavy-Tailed Distributions
  • 2.10.7. Barabási–Albert Model
  • 2.10.8. Model Comparison
  • 2.10.9. Cumulative Distributions
  • 2.10.10. Explanatory Models
  • 2.10.11. References
• 2.11. Lecture 10: Cellular automatons
• 2.12. Overview
• 2.13. Introduction
• 2.14. One-dimensional cellular automata
• 2.15. Wolfram experiments
• 2.16. Turing machines
• 2.17. Lecture 11: Game of Life
  • 2.17.1. Overview
  • 2.17.2. Conway’s Game of Life
  • 2.17.3. Emergence and Common Life Patterns
  • 2.17.4. Conway’s Conjecture and Behaviors
  • 2.17.5. Realism and Instrumentalism
  • 2.17.6. Implementing the Game of Life
  • 2.17.7. Key Takeaways
• 2.18. Lecture 12: Physical modelling
  • 2.18.1. Overview
  • 2.18.2. Administrative Notes
  • 2.18.3. From Discrete to Continuous Models
  • 2.18.4. Physical Modelling
  • 2.18.5. Turing and Morphogenesis
  • 2.18.6. Diffusion Model (Single Chemical)
  • 2.18.7. Reaction–Diffusion Model (Two Chemicals)
  • 2.18.8. Percolation
  • 2.18.9. Percolation Rules
  • 2.18.10. Critical Phenomena and Phase Transitions
  • 2.18.11. Fractals and Fractal Geometry
  • 2.18.12. Attribution
• 2.19. Self-Organized Criticality
  • 2.19.1. Overview
  • 2.19.2. Critical Systems
  • 2.19.3. Sand Piles
  • 2.19.4. Toppling Rule
  • 2.19.5. Implementing the Sand Pile
  • 2.19.6. Heavy-Tailed Distributions
  • 2.19.7. Fractals
  • 2.19.8. Pink Noise
  • 2.19.9. The Sound of Sand
  • 2.19.10. Reductionism and Holism
  • 2.19.11. SOC, Causation, and Prediction
  • 2.19.12. Summary
• 2.20. Lecture 14: Agent-based models
  • 2.20.1. Announcements
  • 2.20.2. Lab Test 2 Preparation
  • 2.20.3. Agent-Based Models
  • 2.20.4. Applications of Agent-Based Models
  • 2.20.5. AI Agents
  • 2.20.6. Risks of AI Agents
  • 2.20.7. Schelling’s Segregation Model
  • 2.20.8. Schelling Model Implementation
  • 2.20.9. Simulation Observations
  • 2.20.10. Sugarscape Model
  • 2.20.11. Emergent Patterns
  • 2.20.12. Emergence
  • 2.20.13. Attribution
• 2.21. Lecture 15: Herds, flocks, and traffic jams
  • 2.21.1. Overview
  • 2.21.2. Traffic Jams
  • 2.21.3. Random Perturbation
  • 2.21.4. Boids
  • 2.21.5. The Boid algorithm
  • 2.21.6. Arbitration
  • 2.21.7. Emergence and Free Will
  • 2.21.8. Notes
• 2.22. Lecture 16: Evolution
  • 2.22.1. Overview
  • 2.22.2. Why evolution is misunderstood
  • 2.22.3. Key ingredients of evolution
  • 2.22.4. Genotypes and fitness
  • 2.22.5. Fitness landscape
  • 2.22.6. Agent and simulation model
  • 2.22.7. Baseline model (no selection)
  • 2.22.8. Differential survival
  • 2.22.9. Mutation
  • 2.22.10. Speciation and genotype distance
  • 2.22.11. Clusters and species
  • 2.22.12. Conclusion
• 2.23. Lecture 17: Evolution of cooperation
  • 2.23.1. Overview
  • 2.23.2. The Problem of Altruism
  • 2.23.3. The Prisoner’s Dilemma
  • 2.23.4. Game of Trust
  • 2.23.5. Robert Axelrod
• 2.24. Lecture 18: Guest Lecture
• 2.25. Lecture 19: AI and Machine Learning / Debates 1
• 2.26. Lecture 20: Project Pitch 1
• 2.27. Lecture 21: AI and Machine Learning / Debates 2
• 2.28. Lecture 22: Project Pitch 2
• 2.29. Lecture 23: AI and Machine Learning / Debates 3
• 2.30. Lecture 24: Project Pitch 3