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. Group Breakout Session
  • 2.3.2. Different types of problems
  • 2.3.3. Modelling Complex Systems - Complexity Science
  • 2.3.4. The Different Scientific Methods
• 2.4. Lecture 3: Python 1
  • 2.4.1. Introduction
  • 2.4.2. JupyterLab
  • 2.4.3. Coding in Python
    • 2.4.3.1. The first program
    • 2.4.3.2. Arithmetic operators
    • 2.4.3.3. Values and types
    • 2.4.3.4. Assignment statements
    • 2.4.3.5. Expressions and statements
    • 2.4.3.6. Script mode
    • 2.4.3.7. Order of operations
    • 2.4.3.8. String operations
    • 2.4.3.9. Comments
  • 2.4.4. Function calls
    • 2.4.4.1. Math Functions
    • 2.4.4.2. Composition
    • 2.4.4.3. Adding new functions
    • 2.4.4.4. Parameters and arguments
    • 2.4.4.5. Variables and parameters are local
    • 2.4.4.6. Fruitful and void functions
• 2.5. Lecture 4: Python 2
  • 2.5.1. Overview
  • 2.5.2. Modulus and Floor Division
  • 2.5.3. Conditionals
    • 2.5.3.1. Boolean operators
    • 2.5.3.2. Logical Operators
    • 2.5.3.3. Conditional Statements
  • 2.5.4. Functions
    • 2.5.4.1. Recursion
    • 2.5.4.2. Return values
  • 2.5.5. While Loops
  • 2.5.6. Cumulative Example - Newton’s Method
• 2.6. Lecture 5: Python 3
  • 2.6.1. Strings
  • 2.6.2. Lists
  • 2.6.3. Dictionaries
  • 2.6.4. Other Topics and Concepts
• 2.7. Lecture 6: Python 4
• 2.8. Lecture 7: Graphs
  • 2.8.1. Introduction
  • 2.8.2. Where you can see graph structures
  • 2.8.3. Definitions
  • 2.8.4. Python Library for Graphs
  • 2.8.5. Chapter 2 notebook:
• 2.9. Lecture 8: Small world graphs
• 2.10. Lecture 9: Scale-free networks
  • 2.10.1. Topics Covered in this Lecture:
    • 2.10.1.1. Learning to Discover Social Circles in Ego Networks:
    • 2.10.1.2. SNAP Facebook Circles Dataset:
    • 2.10.1.3. Heavy-tailed Distributions:
    • 2.10.1.4. Barabàsi-Albert (BA) Generative Model:
    • 2.10.1.5. Explanatory Models
    • 2.10.1.6. Too Many Explanations?
    • 2.10.1.7. Summary:
• 2.11. Lecture 10: Cellular automatons
• 2.12. Lecture 11: Game of Life
• 2.13. Lecture 12: Physical modelling
• 2.14. Lecture 13: Self-organized criticality
• 2.15. Lecture 14: Agent Based Models
  • 2.15.1. Agent-Based Models
  • 2.15.2. Examples of Agent Based Models
  • 2.15.3. Parable of the Polygons Recap
  • 2.15.4. Sugarscape
• 2.16. Lecture 15: Herds, flocks, and traffic jams
  • 2.16.1. Topics covered in this lecture
    • 2.16.1.1. Traffic Jams
  • 2.16.2. Highway Model
    • 2.16.2.1. Boids
    • 2.16.2.2. Emergence and Free Will
• 2.17. Lecture 16: Evolution
  • 2.17.1. Theory of Evolution
  • 2.17.2. Simulating Evolution
  • 2.17.3. Genotypes and Fitness
  • 2.17.4. Notion of Fitness Landscape
  • 2.17.5. Fintess Landscape Class
  • 2.17.6. Agent Class
  • 2.17.7. Simulation Class
  • 2.17.8. Evidence of Evolution
  • 2.17.9. Instrument/Mean Fitness
• 2.18. Differential Survival, Mutation, and Speciation
  • 2.18.1. Differential Survival and Reproduction
  • 2.18.2. Simulation Results, Differential Survival and Reproduction
  • 2.18.3. Mutation
  • 2.18.4. Simulation Results, Mutation
  • 2.18.5. Speciation
  • 2.18.6. Population Dispersion
  • 2.18.7. Looking For New Species
  • 2.18.8. Takeaways
• 2.19. Lecture 17: Evolution of cooperation
  • 2.19.1. Note Taker: Mouner Dabjan
  • 2.19.2. Remarks before the lecture started:
  • 2.19.3. Overview:
  • 2.19.4. Game of Trust:
  • 2.19.5. Relationships:
  • 2.19.6. Jupyter Notebook:
  • 2.19.7. Conclusion:
• 2.20. Lecture 18: Guest Lecture
• 2.21. Lecture 19: AI and Machine Learning / Debates 1
• 2.22. Lecture 20: Project Pitch 1
• 2.23. Lecture 21: AI and Machine Learning / Debates 2
• 2.24. Lecture 22: Project Pitch 2
• 2.25. Lecture 23: AI and Machine Learning / Debates 3
• 2.26. Lecture 24: Project Pitch 3