An introduction to Reinforcement Learning (Spring 2017) - Analysis of the reinforcement learning model, studying of the mathematical theoretical formulations and exploration of openAI environments and algorithms application.
   Deep Learning (Spring 2017) - Jupyter notebooks for the Deep Learning class. The three notebooks contain implementation of NNs, CNNs and RNNs using Tensorflow and Numpy.
   Algorithmic Machine Learning (Spring 2017) - Jupyter notebooks for the AML class. Every notebook presents a case study performed with different machine learning algorithms and various datasets.
   Advanced Statistical Inference (Spring 2017) - Jupyter notebooks for the ASI class. Every notebook is an application of a different topic (Bayesian Linear Regression, Bayesian Logistic Regression, Gaussian Processes, K-Means Clustering, PCA).
   A pilot study on mouse and gaze correlation (Fall 2015) - The aim of this study is to build a methodology in order to find a correlation between gaze and mouse behaviours in the context of simple computer graphics applications, enhancing computational performances. Goal achieved exploiting a multiclass decision forest algorithm. Future applications may involve remote 3D rendering and web-search data analytics.
   hackerrank - A repo updated every while and then with solutions to hackerrank challenges.
   SoftDevNinja - A container for the SoftDev class challenges.