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Publisher:HCAIM consortium

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HBO/WO

MBO

September 1, 2023

Practical: Presenting statistical data

Understand and demonstrate the data representation for AI and ML modelling. Apply a range of plotting techniques to visualise data. Be able to check for fairness, handle outliers and missing data.

Document

HCAIM Consortium

August 23, 2023

Interactive session: Duty Ethics

Learner can apply the knowledge about Duty Ethics, as gained in the lectures, in a debate. Learner is able to explore and evaluate the moral dilemma's regarding a practical example, like autonomous vehicles, from this ethical perspective and can take a stand from different viewpoints (e.g. passenger, pedestrian, programmer).

Website

HCAIM Consortium

August 23, 2023

Lecture: Data and its challenges - Data Regulations, Data Sourcing and HCAI prospective

Understand the importance of various data requisitions Explore the common challenges in managing and utilizing data The ethical consideration of handling data

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HCAIM Consortium

August 23, 2023

Interactive Session: Ethical, Professional and legal Aspects of HCAI applications

The rapid advancements in Artificial Intelligence (AI) have ushered in transformative changes across industries, from healthcare and finance to education and entertainment. As AI technologies become more integrated into our lives, it is imperative to delve into the ethical, professional, and legal dimensions that govern their development and deployment, particularly when the focus is on human-centered applications. To facilitate a deeper understanding of these aspects, an interactive session was organized, bringing together experts, practitioners, and enthusiasts in the field. The interactive session aimed to foster an engaging and informative discussion on the multifaceted aspects surrounding human-centered AI applications. The agenda was structured to cover a range of topics, ensuring a comprehensive exploration of the subject matter.

Website

HCAIM Consortium

August 23, 2023

Lecture: Exploratory Data Analysis

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

August 23, 2023

Lecture: Decision Trees

Learn basics of decision trees

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HCAIM Consortium

August 23, 2023

Interactive session: Value-sensitive Design

Learner can apply VSD in designing and developing data-based services. Learner can apply the three perspectives of VSD. Learner can use the Ethical Matrix to weigh stakeholders, values and impact and can make ethical choices.

Website

HCAIM Consortium

August 23, 2023

Interactive session: Virtue Ethics

Learner is able to prepare argumentation for an AI related case study. Learner can formulate their own point of view in relation to virtue ethics, and discuss it with peers. Learner is able to create their own personal virtue and value profile.

Website

HCAIM Consortium

August 23, 2023

Tutorial: Forward propagation

Understand the process of a forward pass Understand how to calculate a forward pass prediction, as well as loss unplugged Develop a forward pass using no modules in Python (other than Numpy) Develop a forward pass using Keras

Website

HCAIM Consortium

August 23, 2023

Tutorial: Derivation and application of backpropagation

Develop an understanding of gradient and learning rate Derive backpropagation for hidden and outer layers Implimenting Backpropagation unplugged and plugged using different activation functions

Website

HCAIM Consortium