During my academic career, I’ve taught and mentored thousands of students in machine learning and related fields. I am passionate about breaking down complex concepts into clear, practical, and insightful materials that resonate with learners and professionals alike. I particularly enjoy crafting textbooks that offer accessible introductions to the core ideas of machine learning, bridging the gap between theory and application.
Dictionary of Applied Machine Learning (forthcoming)
Editors: Alexander Jung, Ekkehard Schnoor, Konstantina Olioumtsevits
Language: English
Publisher: Springer (Springer Nature Reference)
ISBN: 978-981-95-3207-0 (Print)
A forthcoming reference work that distills the vocabulary of applied machine learning (concepts, methods, and notation) into a single coherent dictionary for students, researchers, and practitioners. It builds on the open-source Aalto Dictionary of Machine Learning and extends it with entries tailored to applied and industrial ML practice.
Federated Learning: From Theory to Practice
Language: English
Publisher: Springer, 2026
ISBN: 978-981-95-1008-5 (Hardcover), 978-981-95-1011-5 (Softcover), 978-981-95-1009-2 (eBook)
The textbook Federated Learning: From Theory to Practice revolves around a flexible design principle for federated learning systems. This principle, referred to as generalized total variation minimization (GTVMin), serves as a natural analogue of empirical risk minimization (ERM), which underpins classical machine learning systems. The book develops federated learning methods systematically from this perspective and builds directly on my earlier textbook, Machine Learning: The Basics.
Machine Learning: The Basics
Language: English
Publisher: Springer, 2022
ISBN: 978-981-16-8192-9 (Print), 978-981-16-8193-6 (eBook)
The textbook Machine Learning: The Basics offers a comprehensive introduction to the fundamental concepts of machine learning, covering key algorithms and techniques in an accessible way. This book is ideal for newcomers and those seeking to strengthen their understanding of core principles.
Aalto Dictionary of Machine Learning — Special Course Edition
Language: English
Publisher: Aalto University Library
Format: Open access
A curated edition of the Aalto Dictionary of Machine Learning tailored for use in special courses on machine learning. It distills the essential terminology and notation needed to follow ML lectures and read research papers, and is designed to be cited and reused for teaching, slides, and scientific publications, including its underlying LaTeX entries.
Maschinelles Lernen: Die Grundlagen
Language: German
Publisher: Springer, 2024
ISBN: 978-981-99-7971-4 (Print), 978-981-99-7972-1 (eBook)
“Maschinelles Lernen: Die Grundlagen” is the German translation of “Machine Learning: The Basics.” It brings the same clarity and foundational insights to German-speaking audiences, making it a valuable resource for students and professionals in machine learning.
Μηχανική Μάθηση: Τα Βασικά
Language: Greek
Publisher: Εκδόσεις Φούντας (Fountas Books), Athens, 2024
ISBN: 978-960-330-837-9
“Μηχανική Μάθηση: Τα Βασικά” is the Greek translation of “Machine Learning: The Basics.” It makes the book’s three-component view of machine learning (data, model, and loss) accessible to Greek-speaking students and professionals.
Additional Resources
Stay tuned for updates on upcoming books and projects!