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Statistical Machine Learning: Overview and Applications

This is diiP's seventh seminar delivered by Dr. Zografoula Vagena, one of diiP's research associate. More information and material is available on our website: https://u-paris.fr/diip/diip-seminars/ abstract: Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. When statistical techniques and machine learning are combined together create a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. In this tutorial, which consists of two parts, we will explain the basics of statistical modeling in machine learning and introduce Probabilistic Graphical Models (PGMs), which blend ideas from statistical and computer science to produce powerful modeling techniques. In this first part of the tutorial, we will first motivate the need for PGMs and discuss applications where the formers have been very successful. We will then provide details on modeling using PGMs and in the process describe some of the most illustrative examples of them.