Preliminary ProgramΒΆ
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.
Data preparation
Data cleaning and missing data reconstruction
Mean imputation
Interpolation
K-nearest neighbors reconstruction
Image inpainting
Data visualization
Matplotlib
Plotly
Dash
Feature extraction using dimensionality reduction techniques (unsupervised)
Principal Component Analysis (PCA) ==> unsupervised
Variational Autoencoder ==> unsupervised
Data clustering (unsupervised)
K-means
Mini-batch K-means
Super-pixel segmentation
Classification, Segmentation and Regression (supervised)
Random forest
XGBoost
LSTM
Convolutional networks
Model Evaluation
Precision
Recall
F1-score
Confusion matrix
Hyperparameter tuning
Learning curves
Random search
Grid search