AI, Machine Learning & Data

Description

AI, Machine Learning & Data is listed in Fontys programme Ad AD-ICT. INFORMATION ABOUT [A-MA-AMD] AI, MACHINE LEARNING & DATA (30EC) Domain Analysis, Exploratory Data Analysis, Data Understanding, Prediction Modelling 16.1. Content In this semester you will learn: to aggregate and prepare given datasets as well as other (open) datasets and use them in data analysis and identify opportunities for predictive a to use findings from data analysis to preprocess data, apply machine learning algorithms and evaluate the quality and usefulness of produced mo to deliver AI projects that follow the three 'Explainable AI' principles of transparency, interpretability, and explainability. 16.2. Learning outcomes 1. Learning outcome: Professional standard Both individually and in teams, you apply a relevant methodological approach used in the professional field to formulate project goals, involve sta decisions, and deliver reports. In doing so, you keep in view the relevant ethical, intercultural, and sustainable aspects. 2. Learning outcome: Personal leadership You are aware of your own strengths and weaknesses, both in the field of ICT and in your personal development. You choose actions in line with learning attitude. 3. Learning outcome: Explainable AI You deliver AI projects that follow the three 'Explainable AI' principles of transparency, interpretability, and explainability. Explanation: Transparency means that the process by which the used input data results in prediction models is reproducible, reliably described a possibility for humans to comprehend the project cohesion and results by making them comparable to the domain knowledge and baselines. Exp box models into grey/white-box models by having the model draw out its decision making process and/or describe its feature importance. 4. Learning outcome: Data Preparation & Analysis You are able to aggregate and prepare given datasets as well as other (open) datasets and use them in data analysis and identify opportunities fo Explanation: Aggregate means acquiring data from a variety of different sources and in different formats and putting it together into a meaningfu to theories of data quality, in such a way that the process of cleaning and preparing those data is repeatable, transp