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Zoekresultaten (12)

HBO/WO

MBO

23 augustus 2023

Tutorial: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

Website

HCAIM Consortium

23 augustus 2023

Lecture: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

Website

HCAIM Consortium

11 mei 2020

Engineering and Integrity - Student handout

A recent engineering graduate struggles economically and considers taking up a lucrative job offer from a defence company. However, she is also opposed to war and wonders about the right thing to do in this situation.

Document

Michael Klenk

11 mei 2020

Engineering and Integrity - Teacher version

A recent engineering graduate struggles economically and considers taking up a lucrative job offer from a defence company. However, she is also opposed to war and wonders about the right thing to do in this situation.

Document

Michael Klenk

23 augustus 2023

Lecture: Utilitarianism

Learner understands the principle of utility. Learner understands the concept of consequence. Learner understands the 'greatest happiness principle'.

Website

HCAIM Consortium

3 augustus 2018

Leading to success: SMART tools

The toolkits help improve labour marketability within the field of public policy. Targeting public policy students, young researchers and practitioners, these toolkits: share the practical utility and relevance of a given skill-set comprehensively distill where to find further information on a given skill-set online

Website

Mindel van de Laar

23 augustus 2023

Practical: Applying and evaluating privacy-preserving techniques

Gain practical skills to mitigate privacy leakages by applying Differential Privacy How to anonymize datasets with Differential Privacy How to train ML models with Differential Privacy Understanding the difference between data anonymization and privacy-preserving model training Study the trade-off between privacy preservation (anonymization) and utility (model quality, data accuracy)

Website

HCAIM Consortium

23 augustus 2023

Lecture: Introduction to privacy and risk

Obtain a general understanding of the notion of privacy. Understand the difficulties and pitfalls of data privacy analysis and detection of personal data. Understand the trade-off between anonymization and data utility (no free lunch). Understand the difference between data security and data privacy. Learn the basic principles of anonymous communication and TOR. Discern, investigate and discuss key risks which AI and Machine Learning models introduce

Website

HCAIM Consortium

14 december 2017

Handleiding RefWorks (Engelstalig)

Deze handleiding is te gebruiken na de cursus RefWorks die maandelijks in de bibliotheek van de UvA wordt gegeven. Uitleg wordt gegeven over het: • creëren van een literatuurbestand: referenties importeren, handmatig invoeren en aanpassen • beheren van het literatuurbestand: ordenen in folders, verwijderen en zoeken • gebruiken van referenties in Word: citaties, literatuurlijst, aanpassingen. Datum laatste update: 29-10-2013

WoDocument

Olga Marx, Drs Joke Sterringa, Drs Noor Breuning, Dr Mariëtte van Selm

23 augustus 2023

Tutorial: Decision Networks

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium