LTAT.05.013 Data Systems Research Seminar
Lectures: Narva mnt 18 - 2047
Schedule: Tuesday. 10.15 - 12.00 week 2-16
Instructor: Radwa El Shawi
Info
The primary objective of this postgraduate seminar is to provide participants with a deep and comprehensive understanding of two critical aspects of contemporary machine learning: Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI). The course aims to delve into the advanced concepts, methodologies, and applications of AutoML and XAI, equipping participants with the knowledge and skills necessary to navigate and contribute to the evolving landscape of machine learning.
Learning outcomes
After passing the course student
- Understand the fundamental concepts and principles behind AutoML.
- Explore various AutoML frameworks and tools, including their strengths and limitations.
- Gain hands-on experience in applying AutoML techniques to real-world datasets.
- Evaluate the role of AutoML in enhancing the efficiency and accessibility of machine learning processes.
- Comprehend the significance of interpretability and transparency in AI models.
- Investigate different methods and algorithms for achieving explainability in AI systems.
- Explore the synergy between AutoML and XAI for building robust and interpretable models.
- Identify scenarios where the combination of AutoML and XAI can provide enhanced model performance and understanding.