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From graphs to neural networks: complexity and simplicity in the framework of mathema9cs Martha-Ivon Crdenas Ar8ficial intelligence research group: So? Compu8ng SOCO. UPC Systems Pharmacology and Bioinforma8cs group. Ins8tut de Neurocincies.

  1. From graphs to neural networks: complexity and simplicity in the framework of mathema9cs Martha-Ivon Cárdenas Ar8ficial intelligence research group: So? Compu8ng SOCO. UPC Systems Pharmacology and Bioinforma8cs group. Ins8tut de Neurociències. UAB mcardenas@cs.upc.edu

  2. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Learning from Graphs: pa<ern recogni9on To bridge AI with pa<ern modelling in secondary/high school To introduce the concept of computa9onal sciences Not only sta9s9cs data but also rela9onal data STEAM methodology can help us with this task

  3. Graphs in a nutshell Simplicity Powerful analysis Not only about geometry Visualiza9on of interacions Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020)

  4. Graphs in a nutshell Simplicity Powerful analysis Not only about geometry Visualiza9on of interacions Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020)

  5. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020)

  6. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Networks: airplains in Europe

  7. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Networks: Facebook

  8. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) y = mx + b Neuron Linear perceptron

  9. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) y = mx + b Neuron Linear perceptron - activation function

  10. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) y = mx + b Neurons Connected linear perceptrons

  11. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Connected linear perceptrons Neural network

  12. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Graph Adjacency Laplacian Degree matrix matrix matrix

  13. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Learning Giving math sense to neural networks

  14. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Classification Clustering Rules

  15. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Decoding the meaning of the data Minimize the error: backpropaga8on

  16. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Decoding the meaning of the data Minimize the error: backpropaga8on

  17. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020)

  18. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020)

  19. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) 2D visualiza9on

  20. cytoscape.org

  21. Zometool

  22. Funny classifica9on

  23. Martha-Ivon Cárdenas: From graphs to neural networks: complexity and simplicity in the framework of mathema8cs (AI4ME 2020) Thank you mcardenas@cs.upc.edu

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