Introduction to Machine Learning Workshop
Introduction to Machine Learning Workshop
The IEEE Student Branch at the University of Liverpool is pleased to invite you to an engaging and beginner-friendly workshop on Machine Learning.
Curious about Machine Learning but find most courses too technical or overwhelming? Join us for an accessible and engaging session designed to help you find your starting point and discover where your interests fit best.
Workshop Goal: By the end of this session, you'll have a better understanding of where your interests fit within the Machine Learning landscape and how to start shaping your own project ideas.
In this session, our guest speakers will introduce the main pillars of Machine Learning, sharing both theoretical insights and real-world applications.
Topics Covered:
Introduction to ML and Classical Algorithms – Tymofii Prokopenko
Bio: Tymofii Prokopenko is a third-year PhD student in the Centre for Doctoral Training in Distributed Algorithms. His research focuses on combinatorial game theory and graph theory. Prior to starting his PhD, he worked on real-world problems in the fields of biology and chemical production.
Supervised Learning – Yehor Yudin
Bio: Yehor Yudin is a PhD graduate from the Technical University of Munich and the Max Planck Institute for Plasma Physics. His work has focused on uncertainty quantification, multiscale modelling, high-performance computing, and applying machine learning to computational plasma physics.
Unsupervised and Semi-Supervised Learning – Alex Williams
Bio: Alex Williams is a second-year PhD student working on unsupervised machine learning for image processing in electron microscopy. He is based at the CDT in Distributed Algorithms and collaborates with SenseAI. Outside of research, he enjoys building cars and spending time with his cat.
Reinforcement Learning – Benedict Oakes
Bio: Benedict Oakes is a Postdoctoral Research Associate in the Signal Processing Group. He recently completed his PhD on reinforcement learning for space situational awareness, focusing on using RL to control ground-based sensors for satellite observation.
The workshop is open to all EEE students interested in learning the basics and beyond!
Date: 13 May 2025
Time: 11:00 AM – 3:00 PM
Location: EEE Building, University of Liverpool
Please register via this link: Registration Link
Teams Link: Microsoft Teams link
We look forward to welcoming you to the workshop!
Sahar Rahbar
IEEE Student Branch Chair
University of Liverpool
Date and Time
Location
Hosts
Registration
- Date: 13 May 2025
- Time: 10:00 AM UTC to 02:00 PM UTC
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- EEE building
- Liverpool, England
- United Kingdom
- Starts 06 May 2025 11:00 PM UTC
- Ends 12 May 2025 11:00 PM UTC
- No Admission Charge
Speakers
Introduction to ML and Classical Algorithms – Tymofii Prokopenko
Biography:
Tymofii Prokopenko is a third-year PhD student in the Centre for Doctoral Training in Distributed Algorithms. His research focuses on combinatorial game theory and graph theory. Prior to starting his PhD, he worked on real-world problems in the fields of biology and chemical production.
Supervised Learning – Yehor Yudin
Biography:
Yehor Yudin is a PhD graduate from the Technical University of Munich and the Max Planck Institute for Plasma Physics. His work has focused on uncertainty quantification, multiscale modelling, high-performance computing, and applying machine learning to computational plasma physics.
Unsupervised and Semi-Supervised Learning – Alex Williams
Biography:
Alex Williams is a second-year PhD student working on unsupervised machine learning for image processing in electron microscopy. He is based at the CDT in Distributed Algorithms and collaborates with SenseAI. Outside of research, he enjoys building cars and spending time with his cat.
Reinforcement Learning – Benedict Oakes
Biography:
Benedict Oakes is a Postdoctoral Research Associate in the Signal Processing Group. He recently completed his PhD on reinforcement learning for space situational awareness, focusing on using RL to control ground-based sensors for satellite observation.