Data Mining

John Samuel
CPE Lyon

Year: 2017-2018
Email: john(dot)samuel(at)cpe(dot)fr

Creative Commons License

Data Mining

Goals

  1. Artifical Neural Networks
  2. Deep Learning
  3. Reinforcement Learning
  4. Data Licences, Ethics and Privacy

1. Artificial Neural Networks

Artificial neural networks

1. Artificial Neural Networks

Perceptron

Artificial neural networks

1. Artificial Neural Networks

Perceptron: Formal definition

Artificial neural networks

1. Artificial Neural Networks

Perceptron: Formal definition

Artificial neural networks

1. Artificial Neural Networks

Perceptron: Steps

  1. Initialize weights and threshold
  2. For each example (xj, dj) in training set
    • Calculate the weight: yj(t)=f[w(t).xj]
    • Update the weights: wi(t + 1) = wi(t) + (dj-yj(t))xj,i
  3. Repeat step 2 until the iteration error 1/s (Σ |dj - yj(t)|) is less than user-specified threshold.

1. Artificial Neural Networks

Backpropagation

2. Deep Learning

Deep neural networks

2. Deep Learning

Applications

2. Deep Learning

Convolutional deep neural networks

3. Reinforcement Learning

4. Data Licences, Ethics and Privacy

4. Data Licences, Ethics and Privacy

Big Data

4. Data Licences, Ethics and Privacy

Privacy

4. Data Licences, Ethics and Privacy

Open Data

4. Data Licences, Ethics and Privacy

Linked Open data cloud

4. Data Licences, Ethics and Privacy

Archived data

References

Online resources

References

Colors

Images