Developing a Machine Learning Curriculum
It is almost 10 years since I started on the tenure track at Aalto University (Finland). From the very beginning, I had the unique opportunity to contribute to shaping and enhancing the Machine Learning curriculum at our university.
Over the past decade, I have designed and implemented core courses for the machine learning curricula at Aalto University at both the Bachelor and Master levels. These courses have not only benefited degree-seeking students but also reached beyond, offering opportunities for adult learners through the Finnish Institute of Technology (FITech.io).
Additionally, I have played a key role in the Human-Centered Machine Learning Summer School (2022), which was organized for the Unite! European University Network, fostering collaboration and cross-border learning.

🚀 Reflections on Curriculum Development
Developing a machine learning curriculum is iterative. A curriculum needs to balance theoretical foundations with practical applications. Three core principles have driven my approach:
-
Practical Relevance: The design of a course starts with writing down the learning goals. These goals include very concrete skills and core theoretical concepts. It can be effective to motivate and demonstrate theoretical concepts by every-day life applications.
-
Student-Centric Design: I view student feedback as important as peer reviews for my research papers. Similar to the peer review process of journals I prepare response letters to explain how sutdent feedback has been taken into account.
-
Collaboration & Inclusivity: By working with partners like FITech and Unite!, the curriculum extends beyond Aalto University, offering access to a broader learning community, including adult learners and international students.
These principles foster a curriculum that grows alongside advancements in machine learning and AI.
📣 Testimonial from a Co-Lecturer
“…I like to take the opportunity to express that, in my opinion, Alex has generated a very strong, didactically excellent course with a good focus on the necessary basic concepts and principles. It mixes theory and focused exercises with a machine learning project in which students grow while applying their learned knowledge on actual data. From last year to this year, the course has been further improved significantly by rigorously addressing the feedback collected by the students.”
📣 Testimonial from VP Education of Aalto University
“…I learnt to know Alex in 2015 in my earlier role, VP Education of Aalto University, when Alex joined the faculty of Aalto University School of Science as an Assistant Professor. My first impression was his dedication and passion to create powerful learning experiences for all his students. It was no surprise that the student feedback was extremely positive from day one…”
🌐 Looking Ahead
As the field of machine learning evolves, so must our university curricula. My current focus is on integrating more content related to explainable AI (XAI), federated learning, and trustworthy AI. These are areas of growing importance in both academia and industry. We need to equip students with required skills to build human-centered and trustworthy AI.
There is also growing demand for education in legal literacy in AI and data-driven technologies. We need to reshape tech-focused curricula in order to cover the intersection of AI and law. This includes courses that provide students with an understanding of key legal concepts such as data privacy regulations (like GDPR), AI ethics, and accountability frameworks. These additions will enable students to grasp the legal and ethical implications of deploying AI systems, which is increasingly essential for roles in both academia and industry.