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Machine Learning

Jun 16, 2019

Obtained with a grade of 100%, multiple subject have been treated:

  • Supervised Learning (Linear Regression, Logistic Regression, SVMs, Neural Networks)
  • Unsupervised Learning (PCA, K-Means, Univariate/Multivariate Gaussian distribution)
  • Special applications (Recommender systems, large scale ML, distributed systems)
  • Advice on building ML systems (Bias/variance, regularization, evaluating a learning algorithm, learning curves, error analysis, ceiling analysis)

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Thomas Di Martino
PhD Student in AI & Remote Sensing

My research interests include deep learning technologies, automatic feature extraction and computer vision, all of them applied to Remote Sensing problematics, more precisely to Synthetic Aperture Radar (SAR) acquisitions.

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