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Frontiers in Mathematics and Computer Science. Salt Lake City Public Library, SLC, Utah Nazmus Saquib Scientific Computing and Imaging Institute. welcome back!. t oday we will experiment with some code l earn a bit about graph theory and genetic algorithm

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frontiers in mathematics and computer science

Frontiers in Mathematics and Computer Science

Salt Lake City Public Library, SLC, Utah


Scientific Computing and Imaging Institute

welcome back
welcome back!
  • today we will
    • experiment with some code
    • learn a bit about graph theory and genetic algorithm
    • discuss the implications of mathematics research
i nstalling python and pygame
installing python and pygame
  • python is a programming language
  • suitable for beginning and learning programming
  • we will play with some python examples today
a genda day 2
agenda – day 2
  • mathematics
    • chaos theory
      • butterfly effect
      • weather forecast
      • fractal music
      • L-systems
      • social interactions (in facebook)
    • graph theory
      • social interactions example (continued)
agenda day 2
agenda – day 2
  • computer science
    • machine learning
      • big data
      • genetic algorithms
    • data mining
      • sentiment analysis
      • digital humanities
graph theory
graph theory
  • in the context of social interactions
  • can we predict the behavior of a group of people? (given some information)
  • group dynamics
  • graph network
  • node and edge

c ulture hubs
culture hubs
  • degree of a node

very primary types of analysis
(very primary) types of analysis
  • power
    • (who’s The Guy?!)
    • related to the degree of a graph
  • closeness
    • how many people do I need to know to reach someone else asap?

primary types of analysis
(primary) types of analysis
  • betweeness
    • who can get me to the most important people asap?
    • asap:shortest path in the graph
    • number of times I need to go through someone to reach someone else
primary types of analysis1
(primary) types of analysis
  • betweeness

(only equation in the slides, I promise!)

this is to show you how easy it is to calculate such metrics

e xample 15 th century florence
example – 15th century Florence
  • Medici family was less powerful than others
  • they ended up dominating
  • why is that so?
  • betweeness score
  • Medici: 0.52
  • second largest: 0.25
  • moral: networking is important!
  • Medici held the network together
artificial intelligence
artificial intelligence
  • machine learning is the development of algorithms from which programs can learn
  • what can they learn?
  • what can they do with the training?
  • training datasets
invitation to big data
invitation to big data
  • we deal with exabytes of data nowadays
  • 1 exabyte = 1 099 511 627 776 megabytes
  • 2147483 hard disks (that are each 500 GB) !!
  • how do we make sense of such a huge amount of information?
  • opportunities in supercomputing and machine learning
flavor of artificial intelligence
flavor of artificial intelligence
  • Terminator 2 was not quite right, robots haven’t taken over yet
  • but we can use AI in other ways
  • evolutionary algorithms
  • set a goal, evolve your given information towards the goal
  • genetic algorithm
genetic algorithm
genetic algorithm
  • say, you would like to break someone’s password
  • you can try all random combinations
  • or you can do some intelligent guesses
  • how can we simulate this process for a computer?
simple genetic algorithm
simple genetic algorithm
  • start with “;wql* opqlq”
  • end goal: “hello world”
genetic algorithm1
genetic algorithm
  • treat these characters as genes!
  • genes can mutate, right?
genetic algorithm2
genetic algorithm
  • but wait, the program should not accept every mutation
  • how does it know it is closer to the goal?
  • how can we find the difference between two sets?
  • Euclidean distance
genetic algorithm3
genetic algorithm
  • fitness test: is a gene fit to pass?
  • If the difference between source and target is lower, we accept the mutation.
  • intermediate results are important too!
  • in reality, you would derive a good fitness function that would produce “intelligent” results
  • if you were writing a password breaker, you wouldn’t know the password to begin with!
genetic algorithm4
genetic algorithm
  • text evolution example (
  • music evolution example (
end of day 2
end of day 2
  • resources can be found at
    • code examples
    • things to try out
  • thanks for attending! 