Intelligent Systems and Machine Learning

Research Cluster


Description

Who are we?

Sponsored by Faculty of Science and Technology (FST) of Athabasca University (AU) and established in September of 2021, the Intelligent Systems and Machine Learning (ISML) research cluster is an interdisciplinary group of scholars and students who share common interests in this research area.

The ISML cluster meets regularly in the form of workshops and asynchronous conversations to communicate and share research achievements, issues, and opportunities on intelligent systems and machine learning, and to provide a forum for collaboration on research involving learning algorithms, their applications, the social and cultural impact, and ethical considerations of artificial intelligence.

Intelligent Systems

An inteligent systems operates in both real-worlds or virtual worlds, possess primary cognitive abilities such as perception, action control, reasoning, or language use, and exhibits intelligent behavior supported by abilities such as rationality, adaptation through learning, or the ability to explain the use of its knowledge by introspection.

Intelligent systems are poised to fill a growing number of roles in today's society, including factory automation, field and service robotics, assistive robotics, military applications, medical care, education, and intelligent transportation. T he emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data.

Machine Learning

Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis.


Vision

The ISML cluster of FST seeks to provide a collaborative and supportive research environment for faculty and students in undergraduate and master programs and to create an environment for learner-focused approach of the teaching to equip our students with advanced ISML research and development techniques.


Goals

  • Building a community of practice among faculty and students interested in intelligent systems and machine learning.
  • Fostering partnerships with IS or ML experts within and outside of the university.
  • Developing strength in the areas of IS and ML to generate and apply practice-based research.
  • Ensuring students have a strong foundation in IS and ML theory and practice.
  • Building infrastructure for IS and ML research, including developing the necessary resources.