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Data Science

Applications of Data Science to Natural Sciences

This module will present in a series of Masterclasses applications of data science to the cutting edge questions of research in various fields of Natural Sciences.

Alessandro Treves: Can information theory help understand information processing in our brain? (2h)

Carlo Baccigalupi: Data Science in Cosmological Observations (2h)

Ethics in ML and AI

This module will provide an introduction and overview to ethical issues in ML and AI, and illustrate them with contributions from guest speakers from a variety of fields. Topics covered will include bias in supervised systems, fairness, legal aspects of AI, data collection and exploitation, privacy, gender and racial inequalities, and many others. 

Unsupervised Learning and Non-parametric Methods

Course description: The aim of this course is to introduce the essential tools of unsupervised learning and dimensional reduction. These tools are of increasing use in preprocessing large databases to obtain human-readable information. We will present the most relevant dimensionality reduction algorithms for linear data manifolds, curved manifolds, and manifolds with arbitrarily complex topologies. We will then introduce a selection of approaches for estimating the probability density and the intrinsic dimension of the data manifold.

Bayesian Inference I

Course description: Probabilistic models are an appealing way to reason about systems that exhibit intrinsic and/or observational uncertainty. An important question in such models is how observational data can be used to reduce/quantify such uncertainty, leading to improved predictions and scientific discovery. Bayesian inference provides a mathematically coherent framework to incorporate knowledge from observations into models, by providing algorithms to compute posterior distributions over unobserved model variables.

Introduction to Statistical Modelling and Inference

Pre-requisites: familiarity with Python and jupyter installed on students’ computers. Course description: this course introduces the fundamentals of statistical methods. The first part will be dedicated to introducing the language and principles of probability theory from both frequentist and Bayesian point of views. We will review the standard probability distributions and describe their main properties.

Current topics in the theory of neural networks: Dynamics and Data

Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.


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