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Computational Sciences - Course Explorer | Minerva Schools

Students learn about models of computation that provide the theoretical basis for modern computer science.

Topics include deterministic and nondeterministic finite state machines, Turing machines, formal language theory, computational complexity and the classification of algorithms.

and what role does a grammar plays in the way we analyze problems, solve problems, communicate with the computer, and even analyze natural languages?

2nd HUMAINT Winter school on Fairness, Accountability and Transparency in Artificial Intelligence

After asuccessful first event, this year edition focuses on the topic of fairness and accountability in AI and it is taking place one week before theACM Conference on Fairness, Accountability, and Transparency (ACM FAT*), a computer science conference with a cross-disciplinary focus that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

She also spent five years at Google, defining the company’s investment and business strategy in Eastern Europe, the Middle East, and Africa.Cosmina is interested in working with unconventional data to improve policy makers’ understanding of the world.

The findings of her research informed the EU-wide digital agenda policy package and represented the first use of Google data to inform policy design at the EU level.Academically, Cosmina’s expertise lies in international economics and econometrics.

She has worked extensively in the fields of data protection, freedom of information and information technology, having advised on a number of information technology implementations, data sharing projects and statutory reforms.

Theodorou is working at producing techniques and tools for the design, implementation, and deployment intelligent systems, while taking into consideration the socio-economic, legal, and other ethical issues and challenges that arise from integrating AI into our societies.

Human Centric Machine Learning: Feedback loops, Human-AI Collaboration and Strategic Behavior(Manuel Gómez)Withthe advent of mass-scale digitization of information and virtually limitless computational power, an increasing number of social, informationand cyber-physical systems evaluate, support or even replace human decisions using machine learning models and algorithms.

As these decisions become moreconsequential to individuals and society, machine learning models and algorithms have been blamed to play a major role in an increasing number of missteps,from discriminating minorities, causing car accidents and increasing polarization to misleading people in social media.In this lecture, you will learn about a new generation of human-centric machine learning models and algorithms for evaluating, supporting and enhancingdecision making processes where algorithmic and human decisions feed on and influence each other.

These models and algorithms account for the feedbackloop between algorithmic and human decisions, which currently perpetuates or even amplifies biases and inequalities, they learn to operate under differentautomation levels, and anticipate how individuals will react to their algorithmic decisions, often strategically, to receive beneficial decisions.

Moreover, you willget to know about a wide range of applications, from content moderation, recidivism prediction, and credit scoring to medical diagnosis and autonomous driving,where human-centric machine learning can make a positive difference.>>

The reams of data that governments collect about citizens could, in theory, be used to tailor education to the needs of each child or to fit health care to the genetics and lifestyle of each patient.

In the second half, we will bring to light the ethical dimension by examining a concrete example: if tasked with writing a machine learning system to identify children at risk, how should we proceed?

Students will work through a framework for evaluating AI that include the following five questions:1) What and whose goals are being achieved or promised through;2) What structured performance using;3) What division of labour;4) Under whose control and5) At whose expense?

Considering the 'FAT' implications of the use of machine learning within police decision-making (Marion Oswald)This session focuses upon machine learning algorithms within police decision-making, specifically in relation to predictive analytics.

It first reviews the state of the art regarding the implementation of algorithmic tools underpinned by machine learning to aid police decision-making, often linked to the prevention and public protection duties and functions of the police.

For each of these scenarios, we will present concrete research outcomes, propose some short group exercises, and discuss on the need for interdisciplinary and diverse views, impact assessment frameworks and specific adaptations to the concrete application domains.In the second part of the session, we will present the divinAI initiative that researches and develops indicators of diversity of AI research events including gender, academia vs industry, and geographical location.

Master's Programme in Computer, Communication and Information Sciences – Machine Learning, Data Science and Artificial Intelligence

Applicants to the programme must meet the general eligibility and language requirements that are common to all Master's programmes in the field of science and technology.

Excellent candidates with degrees in other fields such as information systems, engineering, natural sciences, mathematics or physics will be considered if they have sufficient studies and proven skills and knowledge in the required areas.

Required background for the Machine Learning, Data Science and Artificial Intelligence (Macadamia) major includes sufficient skills in Knowledge of the following areas is considered an advantage: The student selection process is competitive and the best applicants are selected according to the following evaluation criteria: During the evaluation of eligible applications, the applicants’ previous study success and contents of the previous degree(s) are checked first.

Very good previous study success is expected. This means that the applicant has consistently achieved the best grades throughout the degree studies (very high weighted average grade or GPA).

Relevant work experience, professional certificates and/or online courses are judged case-by-case, but they do not, in general, compensate for the university level studies that include also the theoretical foundations of the required subjects.

In addition to the compulsory application documents, applicants to the programme are requested to provide the following study-option-specific documents: *) The lack of these documents will adversely affect the evaluation of your application.

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