Forthcoming machine learning and AI seminars: February 2026 edition

2026-02-04 · Source: ΑΙhub · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Fundamental Awareness, short

Summary

AIhub has published its "Forthcoming machine learning and AI seminars: February 2026 edition" post, detailing a schedule of free, virtual AI-related seminars from February 4 to March 31, 2026. These events are hosted by various institutions including the University of Oxford, Imperial College London, The University of Manchester, San Francisco University, Google Research, HEC Montreal, Georgia Tech, University of Minnesota, École Polytechnique, Raspberry PI, London School of Economics, Chalmers AI4Science, Machine Learning Tokyo, Carnegie Mellon Libraries, University of Vienna, Flatiron Institute, Research Centre for Intelligent Information Technologies, Georgia Institute of Technology, University of the Arts London, and Polytechnique Montréal. Topics span AI ethics, neurosymbolic systems, DAO governance, on-device AI, urban mobility, mathematical thinking, AI for society, digital health equity, materials chemistry, machine learning optimization, LLMs, distributed training, open AI, social scoring, XAI, generative models, and privacy vulnerabilities in ML.

Key takeaway

For AI Researchers and AI Engineers seeking to expand their knowledge and network, reviewing this seminar list is crucial. Your participation in these free, virtual events offers direct access to diverse topics like AI ethics, neurosymbolic systems, and large language models, potentially informing your current projects or sparking new research directions. Consider registering for relevant sessions to stay current with advancements and engage with leading experts.

Key insights

A comprehensive list of free, virtual AI/ML seminars from February 4 to March 31, 2026, is now available.

Principles

• AI ethics is a recurring seminar theme.
• Practical AI applications are widely discussed.

In practice

• Attend virtual seminars on AI ethics.
• Explore neurosymbolic AI approaches.
• Learn about on-device AI systems.

Topics

• AI Ethics
• Explainable AI
• Large Language Models
• Generative Models
• Distributed AI Systems

Best for: AI Researcher, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by ΑΙhub.