# Preliminary Program

This section lists the techniques that are exposed to the participants. It is worth noting that a variety of techniques are introduced in order to give the participants a general look on the existing technologies but not all of these techniques are used in the practical use cases.  

1. **Data preparation**

   * Data cleaning and missing data reconstruction 

   * Mean imputation 

   * Interpolation

   * K-nearest neighbors reconstruction 

   * Image inpainting

2. **Data visualization**

   * Matplotlib

   * Plotly

   * Dash

3. **Feature extraction using dimensionality reduction techniques (unsupervised)**

   * Principal Component Analysis (PCA) ==> unsupervised 

   * Variational Autoencoder ==> unsupervised 

4. **Data clustering (unsupervised)**

   * K-means 

   * Mini-batch K-means

   * Super-pixel segmentation

5. **Classification, Segmentation and Regression (supervised)**

   * Random forest 

   * XGBoost 

   * LSTM

   * Convolutional networks

5. **Model Evaluation**

   * Precision

   * Recall

   * F1-score

   * Confusion matrix

6. **Hyperparameter tuning**

   * Learning curves

   * Random search

   * Grid search