Universiteit Utrecht (UU) • Master Artificial Intelligence

This cheat sheet contains the most important information from the course and refers to pages in the full summary for extra details. Everything is clearly highlighted.

This document includes a summary of all lectures, lecture notes, screenshots of important lecture slides and extra notes to help understand the contents and concepts better.

This document contains notes and summaries covering the content of the course Social Computing within the Artificial Intelligence Master at Utrecht University. It covers the following topics: - what's social computing - ethics - migration & migrants - social data & digital traces - social network analysis - nlp X social media - reality mining - digital surveys - agent-based modeling - urban computing - AI X social good

This document contains notes and summaries covering the content of the course Pattern Recognition within the Artificial Intelligence Master at Utrecht University. It covers the following topics: - LMs for regression - LMs for classification 1&2 - intro to DL and NN - CNNs - RNNs

This document contains notes and summaries covering the content of the course Natural Language Generation within the Artificial Intelligence Master at Utrecht University.

This document contains notes and summaries covering the content of the course Natural Language Generation within the Artificial Intelligence Master at Utrecht University.

This document contains notes and summaries covering the content of the course Natural Language Generation within the Artificial Intelligence Master at Utrecht University. It is divided into two parts: Explainability and Fairness. It covers the following topics: - intro to nlg - nlg metrics and evaluation - commercial nlg - linguistics, pragmatics and the grecian maxims - REG algorithms

This document contains notes and summaries covering the content of the course Human-Centered Machine Learning within the Artificial Intelligence Master at Utrecht University. It covers the following topics: - intro to FAI - measuring fairness - interventions & making fair systems

This document contains notes and summaries covering the content of the course Human-Centered Machine Learning within the Artificial Intelligence Master at Utrecht University. It covers the following topics: - intro to XAI - interpretable models - model agnostic interpretability methods - neural network interpretability

This document contains notes and summaries covering the content of the course Human-Centered Machine Learning within the Artificial Intelligence Master at Utrecht University. It is divided into two parts: Explainability and Fairness. It covers the following topics: - intro to XAI - interpretable models - model agnostic interpretability methods - neural network interpretability - intro to FAI - measuring fairness - interventions & making fair systems