Machine Learning, Gen AI and Others

Machine Learning- A Starting Point

This workshop offers a practical introduction to machine learning, tailored for students and professionals in Engineering and IT disciplines. It will cover the fundamentals of machine learning, including the main categories of techniques, the basic rules for choosing the right approach, and an overview of widely used libraries such as scikit-learn, TensorFlow, and PyTorch. The focus will be on practical decision-making—how to match techniques to different types of problems—and exploring real-world applications across Engineering and IT contexts. The session will also highlight how foundational knowledge in machine learning can support future research pathways, including Honours, Master's, or PhD studies.

Foundation Models in AI: Language, Vision, and Diffusion

This workshop introduces the core principles of modern large language models (LLMs) and extends them to vision–language and multimodal foundation models. We examine key architectural components and training objectives that underpin these systems. Diffusion models are presented as a complementary generative paradigm within modern AI. The workshop includes practical demonstrations, such as few-shot learning with vision–language models, illustrating how foundation models enable rapid adaptation across tasks.

Responsible Use of GenAI in Academic Research

While generative artificial intelligence (GenAI) tools provide incredible potential, it is essential to understand the risks and ethical considerations involved. This seminar shares with engineering graduate researchers the general guidance provided by the university on what to consider when using GenAI in the research process. Best practices, pitfalls and strategies to ensure responsible use of GenAI in compliance with the university AI policies and guidelines will be discussed.