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
• 2.19. Lecture 13: Self-organized criticality
• 2.20. Lecture 14: Agent-based models
• 2.21. Lecture 15: Herds, flocks, and traffic jams
• 2.22. Lecture 16: Evolution
• 2.23. Lecture 17: Evolution of cooperation
• 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