Diploma in Artificial Intelligence
Harness the Potential of Artificial Intelligence
Artificial Intelligence brings rapid changes to all sectors of society opening opportunities and the need for new solutions. To succeed in the competition organizations and individuals need new technological skills as well as a clear understanding of the big picture of AI. What is AI, what are the current AI technologies, and how can they be developed and deployed?
Check out the New Artificial Intelligence Training Program
Based on the Diploma in Artificial Intelligence training, we created the new Data, Analytics, and AI for Professionals (DAAP) program that brings our best content and trainers focused on data, analytics, and artificial intelligence. The program is held in Finnish in fall 2022.
"Artificial Intelligence is not a futuristic dream. It shapes our world at an unprecedented pace. This program is a deep dive into the latest trends of AI.”
Professor, University of Helsinki
The program builds competencies for the future. After the program, you know how to create new business solutions based on AI, and Streamline and automate your processes with AI solutions.
The program is targeted at business and technology developers who need both practical skills and in-depth understanding in order to utilize artificial intelligence technologies.
The program is suitable for
- Programmers and Developers
- Product Managers
- Business Development Managers and Directors
- Deployment Managers
- Software Architects
IT Managers and Directors
Teemu Roos is a Professor at the Department of Computer Science, University of Helsinki.
Roos' research interests include the theory and applications of artificial intelligence, machine learning, and data science. He also teaches introductory courses on these topics with a total of up to 500 students annually. He has developed applications of AI in areas such as mobile computing, genomics, epidemiology, quantum physics, and digital humanities.
Teemu Roos received a Ph.D. in computer science from the University of Helsinki in 2007.
Laura Ruotsalainen is an Associate Professor of Spatiotemporal Data Analysis for Sustainability Science at the Department of Computer Science at the University of Helsinki.
Her current research interests include the development of computer vision, estimation and machine learning algorithms for creating and using accurate and reliable spatiotemporal data, namely navigation data, especially for the development of autonomous systems enabling sustainable smart cities. For years, she has been teaching courses and supervising research on navigation at the Aalto University and the University of Helsinki and more recently also a course on Computer Vision at the University of Helsinki.
Jaakko Hollmén is Chief Research Scientist at the School of Science and Technology at Aalto University. Hollmén’s research focuses on machine learning, data mining, and artificial intelligence. He has a special interest in predictive analytics.
Hollmén has more than 20 years experience in data analytics, both in manufacturing industry and university research. He is a founding member of Northlet Ltd., which offers analytics services and consulting for companies aiming to improve their usage of analytical tools. In 2017, Hollmén was a key organizer of ECML-PKDD 2017 – the biggest conference in machine learning and data mining in Europe.
Alex Jung is Assistant Professor for Machine Learning within the Department of Computer Science at Aalto University.
He heads the research group "Machine Learning for Big Data" which studies fundamental limits and efficient algorithm for machine learning problems involving massive datasets. Jung is teaching the main courses at Aalto University on machine learning and artificial intelligence. The recent edition of Artificial Intelligence and Machine Learning: Basic Principles have attracted around 500 and 900 students, respectively.
Michael Mathioudakis is an Assistant Professor at the Department of Computer Science, University of Helsinki.
His research interests include web mining, data science, and optimized data processing. He also teaches postgraduate courses on computational social science, network analysis, and data management.
Prof. Mathioudakis received a PhD in computer Science from the University of Toronto in 2013.
Henri Schildt is a tenured professor with a joint appointment at the Aalto University School of Business (Management & Organizations) and the School of Science’s Department of Industrial Engineering and Management.
His research interests span technology strategy, organizational change, and strategy process (with specific interest in reasoning under uncertainty and the use of data analytics). He is currently heading two research programmes on data-driven management and the management of digitalization in Finnish manufacturing companies. Henri Schildt completed his PhD at Helsinki University of Technology in 2007.
Antti Ukkonen is an Academy Research Fellow at the Department of Computer Science, University of Helsinki. He has 15 years of experience in research and development of data analysis algorithms.
Prior to joining UoH, Antti has worked as a data scientist at Yahoo! Research, Helsinki Institute for Information Technology HIIT, and Finnish Institute of Occupational Health. He has published over 40 scientific articles in international journals and conferences on the topics of data mining, machine learning, and algorithms. He obtained his doctoral degree from Helsinki University of Technology in 2008.
Dr. Marko Turpeinen is an Adjunct Professor at Aalto University. He is also founder and CEO of a recently established data sharing company called 1001 Lakes.
He was previously the Finnish Node Director of EIT Digital, a Knowledge an Innovation Community of the European Institute of Innovation and Technology and Professor in Media Technology at The Royal Institute of Technology (KTH) in Stockholm. He has extensive industrial experience from the media industry, as between 1996 and 2005 he worked in various executive positions at Alma Media Corporation, a Finnish media company.
His current academic research addresses issues in customized media content, human-centric approach to personal data, and the role of AI and algorithmic power in networked society. He has a Doctor of Technology Degree in Computer Science from Helsinki University of Technology (now Aalto University) and a Master of Science Degree in Media Arts and Sciences from Massachusetts Institute of Technology (MIT).
Assistant Professor, Aalto University
Dr. Arno Solin is Assistant Professor in Machine Learning at the Department of Computer Science, Aalto University. Previously, he has worked in industry as an algorithm developer and team lead, and still holds ties with several tech startups.
He was a postdoctoral Visiting Research Fellow at University of Cambridge until June 2018, after which he joined Aalto as a faculty member. Prof. Solin is the winner of several prizes, hackathons, and modelling competitions, including the Schizophrenia Classification Challenge on Kaggle, the ISIF Jean-Pierre Le Cadre Best Paper Award, and a teaching award for his introductory course in Artificial Intelligence. His research group mainly focuses on temporal models combining statistical machine learning with applications in sensor fusion, computer vision, finance, robotics, and online decision making.
Professor, Department of Computer Science, University of Helsinki Director of Data Science Master’s Program
Jussi Kangasharju received his MSc from Helsinki University of Technology in 1998. He received his Diplome d'Etudes Approfondies (DEA) from the Ecole Superieure des Sciences Informatiques (ESSI) in Sophia Antipolis in 1998.
In 2002 he received his PhD from University of Nice Sophia Antipolis/Institut Eurecom. In 2002 he joined Darmstadt University of Technology (TUD), first as post-doctoral researcher, and from 2004 onwards as assistant professor. Since June 2007 Jussi is a professor at the department of computer science at University of Helsinki. Between 2009 and 2012 he was the director of the Future Internet research program at Helsinki Institute for Information Technology (HIIT). Since 2018 he is the director of the data science master’s program at University of Helsinki. Jussi's research interests are information-centric networks, edge and cloud computing, content distribution, opportunistic networks, and green ICT. He is a member of IEEE and ACM.
Kari Hiekkanen is a Research Fellow at Department of Computer Science at Aalto University. He has extensive experience in various IT management and leadership roles in R&D and management consulting.
Hiekkanen has over 20 years of experience in combining IT and strategy in various industries. He has a solid knowledge of IT management practices, enterprise architecture, and IT governance both, as a practitioner and, as an educator. His research interests include the industrial Internet and digitalization of Industries. Hiekkanen is an expert on machine learning, deep learning, and artificial intelligence.
Pauliina Ilmonen is Associate Professor in Statistics at Aalto University School of Science, Department of Mathematics and Systems Analysis.
Ilmonen has over a decade of teaching experience from various universities. She teaches several Bachelor’s and Master’s level courses at Aalto University, and she is the responsible teacher of a minor in Statistics. She is known for her ability to discuss complicated matters in an understandable way. Pauliina Ilmonen is the chair of the Finnish Statistical Society, and she belongs to the European Regional Committee of Bernoulli Society. She loves statistics and she participates actively in public discussions related to statistics.
In her research work, Pauliina Ilmonen focuses on topics in the field of mathematical statistics. Her research group’s most significant research topics are multivariate extreme value theory, invariant coordinate selection (ICS), independent component analysis (ICA), functional data analysis (FDA), change point analysis, and analysis stationary processes. Also pure mathematics is close to Pauliina’s heart. Her research group’s interest there lies on characteristics of meet and join (hyper)matrices. In addition to deriving theoretical results, her group is working on applied topics related to cancer epidemiology and the epidemiology of viruses. Doing applied work enables to contribute to research that may have significant public health implications.