The Masters Series – Semi-Supervised Learning
with Dr Brian Mac Namee
For many data analytics scenarios, while data is freely available, labelled data can be very scarce and expensive to collect. Semi-supervised machine learning algorithms couple small amounts of labelled data with large amounts of unlabelled data to build predictive models. This course will explore the most important semi-supervised machine learning techniques and explore their applications and how they can be put to practical use.
Through real world examples, discussions, and live code demonstrations this one-day workshop, designed for analytics professionals, introduces the most important semi-supervised learning techniques and how they can be used.
Register Your Interest Now
Info and Costs
Date : Tuesday 15th September and Thursday 17th September
Time : 9.30am – 1 pm
Duration : 1 day
Price : €325
Discounts are available for multi-class passes.
Location : Online Classroom
Should I Attend?
To attend this Master Class you should be familiar with fundamental concepts in data manipulation, descriptive statistics, and machine learning. Specifically, you should be comfortable building and evaluating classification models (using techniques such as logistic regression, decision trees, support vector machines or random forests). The live code demonstrations during the workshop will use the Python programming language and relevant Python packages (e.g. pandas, scikit-learn, and keras). While familiarity with these is not required it would be useful. A list of specific functionalities with which you should be familiar, and suggested online revision materials, will be circulated before the workshop.
What will I Learn?
This workshop has been designed to equip delegates with the most important semi-supervised learning techniques, and an understanding of how they should be applied to build real-world-relevant solutions. After completing the workshop delegates will be able to:
- Understand the distinctions between semi-supervised, supervised and unsupervised machine learning.
- Apply semi-supervised learning techniques including pseudo-labelling, co-training, and active learning
- Evaluate semi-supervised machine learning systems.
Dr. Brian Mac Namee
Dr. Brian Mac Namee, Director of Training at Krisolis, has over two decades of experience in data analytics lecturing, training, research, and consultancy. With particular expertise in analytics fundamentals, Brian has delivered analytics training nationally and internationally for world-leading organisations as well as mentored analytics teams at companies ranging from large multi-nationals to small start-ups.
As an academic, Brian manages a group of researchers focusing on analytics and regularly presents and publishes both nationally and internationally on their work. His research interests lie in the areas of artificial intelligence, machine learning, predictive analytics, and data visualisation. He is a co-author of the textbook “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” published in 2015 with MIT Press. Brian is Director of the Science Foundation Ireland funded Centre for Research Training in Machine Learning.