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. Web of Life
  • 2.3.2. Complexity Science
  • 2.3.3. Attributions
• 2.4. Lecture 3: Python 1
  • 2.4.1. Accessing Jupyter Hub
  • 2.4.2. Jupyter Notebook Cells
  • 2.4.3. Python Basics
  • 2.4.4. Strings
  • 2.4.5. Comments
  • 2.4.6. Functions
• 2.5. Lecture 4: Python 2
  • 2.5.1. Floor Division and Modulus
  • 2.5.2. Boolean Expressions
  • 2.5.3. Logical Operators
  • 2.5.4. Conditional Execution
  • 2.5.5. Chained and Nested Conditionals
  • 2.5.6. Recursion
  • 2.5.7. Infinite Recursion and Debugging
  • 2.5.8. Keyboard Input
  • 2.5.9. Boolean Functions
  • 2.5.10. More on Recursion
  • 2.5.11. Reassignment and Updating Variables
  • 2.5.12. The while Statement
  • 2.5.13. Break and Square Roots
• 2.6. Lecture 5: Python 3
  • 2.6.1. Strings
    • 2.6.1.1. A String is a Sequence
    • 2.6.1.2. Length
    • 2.6.1.3. String Traversal
    • 2.6.1.4. String Slices
    • 2.6.1.5. Strings are Immutable
    • 2.6.1.6. String Methods
    • 2.6.1.7. The in Operator
    • 2.6.1.8. String Comparison
  • 2.6.2. Lists
    • 2.6.2.1. Lists as Sequences
    • 2.6.2.2. Lists are Mutable
    • 2.6.2.3. List Traversal
    • 2.6.2.4. List Operations
    • 2.6.2.5. List Slices
    • 2.6.2.6. List Methods
    • 2.6.2.7. Map, Filter, and Reduce
    • 2.6.2.8. Lists and Strings
    • 2.6.2.9. Objects and Values
    • 2.6.2.10. List Arguments
  • 2.6.3. Dictionaries
    • 2.6.3.1. Dictionaries as Mappings
    • 2.6.3.2. Dictionary Operations
    • 2.6.3.3. Counters
    • 2.6.3.4. Dictionary Traversal
    • 2.6.3.5. Reverse Lookup
    • 2.6.3.6. Dictionaries and Lists
  • 2.6.4. Best Practices
• 2.7. Lecture 6: Python 4
  • 2.7.1. Tuples
  • 2.7.2. Tuples vs. Lists in Python
  • 2.7.3. Characteristics:
  • 2.7.4. Zip Function
    • 2.7.4.1. Dictionaries with Tuples:
  • 2.7.5. Classes and Objects
    • 2.7.5.1. Class Implementation Examples
  • 2.7.6. Inheritance
  • 2.7.7. Summary
• 2.8. Lecture 7: Graphs
  • 2.8.1. Graph Types
  • 2.8.2. Graph Traversal
  • 2.8.3. Graphs in Python
    • 2.8.3.1. Complete Graphs
    • 2.8.3.2. Random Graphs
    • 2.8.3.3. Connectivity
• 2.9. Lecture 8: Small world graphs
  • 2.9.1. Review Lecture 7: Graphs
  • 2.9.2. Stanley Milgram + the Milgram Experiment
  • 2.9.3. The Small World Experiment
  • 2.9.4. Watts-Strogatz Graphs
  • 2.9.5. Attribution
• 2.10. Lecture 9: Scale-free networks
  • 2.10.1. Quickly Recapping Last Thursday’s Class:
  • 2.10.2. Today’s Class:
  • 2.10.3. Ego Networks
  • 2.10.4. Julian McAuley & Jure Leskovec
  • 2.10.5. Synthesizing the data
    • 2.10.5.1. Imports and Setup
    • 2.10.5.2. Approximate Algorithms
    • 2.10.5.3. Analyzing the Results
    • 2.10.5.4. Constructing & Comparing the WS graph
  • 2.10.6. Degree
    • 2.10.6.1. Heavy-Tailed Distribution
  • 2.10.7. Barabási -Albert (BA) Generative Model
  • 2.10.8. Cumulative Distributions
• 2.11. Lecture 10: Cellular automatons
  • 2.11.1. What is Cellular Automata
  • 2.11.2. What is 1-D Cellular Automata
  • 2.11.3. Wolfram’s Cellular Automata Experiments
  • 2.11.4. Classifying Cellular Automata Experiments
  • 2.11.5. Turing Machine
  • 2.11.6. Universality
  • 2.11.7. Principle of Computational Equivalence
  • 2.11.8. Attributions
• 2.12. Lecture 11: Game of Life
  • 2.12.1. Quick Facts about Conway:
  • 2.12.2. Introduction to Game of Life (GoL)
  • 2.12.3. Why Care??
  • 2.12.4. Why so Popular??
  • 2.12.5. Rules of the Game
  • 2.12.6. Common Patterns in GoL:
    • 2.12.6.1. Beehive (Stable)
    • 2.12.6.2. Toad (Oscillator)
    • 2.12.6.3. Glider (Moving Spaceship)
    • 2.12.6.4. r-Pentomino (Chaotic Evolution)
  • 2.12.7. Pattern Libraries
  • 2.12.8. Conway’s Conjecture
  • 2.12.9. Scientific Realism vs. Instrumentalism
  • 2.12.10. Attributions
• 2.13. Lecture 12: Physical modelling
  • 2.13.1. Lecture Summary
  • 2.13.2. Physical Modelling Overview
  • 2.13.3. Diffusion
  • 2.13.4. Reaction-Diffusion
  • 2.13.5. Percolation
  • 2.13.6. Fractals
  • 2.13.7. Additional Notes
• 2.14. Lecture 13: Self-organized criticality
  • 2.14.1. Critical Systems
  • 2.14.2. Sand Pile Model
  • 2.14.3. Heavy-Tailed Distribution
  • 2.14.4. Fractals
  • 2.14.5. Pink Noise
  • 2.14.6. Reductionism and Holism
  • 2.14.7. Causation and Prediction
• 2.15. Lecture 14: Agent-based models
• 2.16. Lecture 15: Herds, flocks, and traffic jams
• 2.17. Lecture 16: Evolution
• 2.18. Lecture 17: Evolution of cooperation
  • 2.18.1. The Evolution of Trust Simulation
    • 2.18.1.1. Play Styles
    • 2.18.1.2. Mistakes
    • 2.18.1.3. Conclusions
  • 2.18.2. Robert Axelrod and the Prisoner’s Dilemma
    • 2.18.2.1. The Problem of Altruism
    • 2.18.2.2. Conclusions
• 2.19. Lecture 18: Guest Lecture
• 2.20. Lecture 19: AI and Machine Learning / Debates 1
• 2.21. Lecture 20: Project Pitch 1
• 2.22. Lecture 21: AI and Machine Learning / Debates 2
• 2.23. Lecture 22: Project Pitch 2
• 2.24. Lecture 23: AI and Machine Learning / Debates 3
• 2.25. Lecture 24: Project Pitch 3