Inhalt

[ 993MLPEEAIV20 ] VL Explainable AI

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M2 - Master's programme 2. year Computer Science Marc Streit 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2020W
Objectives Visualization not only can help get insight into our data or present it in a clearer way, but is also a very powerful tool for explaining complex machine learning algorithms. This lecture on the topic of Explainable AI (xAI) will give an overview of WHY we want to visualize, WHO uses xAI, WHAT to visualize; HOW to visualize and WHEN to visualize.
Subject Goals of Explainable AI; WHY Visualize: Interpretability & Explainability, Debugging & Improving Models, Comparing & Selecting Models; WHO Uses xAI: Model Developers & Buildings, Model Users, Non-experts; WHAT to Visualize in xAI: Computational Graph & Network Architecture, Learned Model Parameters, Individual Computational Units, Neurons in High-dimensional Space, Aggregated Information; HOW to Visualize in xAI: Node-link Diagrams for Network Architecture, Dimensionality Reduction & Scatter Plots, Line Charts for Temporal Metric; Instance-based Analysis & Exploration, Interactive Experimentation, Algorithms for Attribution & Feature Visualization; WHEN to Visualize in xAI: During Training, After Training; Research Directions & Open Problems; Selected Current Research and Case Studies
Criteria for evaluation Written exam (oral exam in exceptional cases)
Methods Slide presentation combined with case studies and in-class exercises.
Language English
Study material Fred Hohman, Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. Additional study material will be provided throughout the semester.
Changing subject? No
Corresponding lecture in collaboration with 993MLPEEAIU20: UE Explainable AI (1.5 ECTS) equivalent to
993MLPEEAIK19: KV Explainable AI (3 ECTS)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment