Diploma in Artificial Intelligence

Aalto University School of Business

How long?

  • 14 days
  • in person

What are the topics?

Aalto University School of Business

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Who should attend

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

About the course

Harness the Potential of Articial Intelligence

Artificial Intelligence brings rapid changes to all sectors of society opening opportunities and needs 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?

Diploma in Artificial Intelligence gives you in-depth understanding of the topic and helps you to understand and apply contemporary AI technologies.

The program utilizes the experts in the field and it is a joint effort of Aalto PRO, University of Helsinki HY+ and Finnish Center for Artificial Intelligence FCAI.

Benefits

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.

Learn to develop and deploy solutions using Artificial Intelligence

Understand what AI is and how it effects your business

Recognize AI technologies and relevant questions of the field

Contents and Schedule

Diploma in Artificial Intelligence consists of six two-day study modules utilizing intensive in-class sessions and learning by doing. Covered topics include fundamentals of data and AI as well as contemporary AI technologies and applications. You will also hear practical case examples from companies like OP, Yle, Stora Enso and Fourkind.

The study modules are complemented by individual exercises and project work, which enables you to apply the acquired skills into your daily work right away. In the last part of the program participants can select between a technical study track requiring programming skills or a business study track focusing on business applications.

Modules

What is Artificial Intelligence?

Location Aalto PRO premises in Helsinki

This module will kick off the AI Diploma. We will describe the contents of the program and construct a roadmap to AI. We will discuss when, why, and how to use AI to improve your business using practical example cases to illustrate the key principles. We will also describe the working methods of the AI Diploma, including projects that you will be able to develop throughout the training.

Learning outcomes

After completing this module, each participant will be able to:

  • understand the basic principles and terminology of AI
  • identify the opportunities enabled by different AI technologies
  • list the requirements for successful AI applications
  • facilitate organizational culture that fosters AI-driven business

Data and AI

  • Introduction to Data Science
  • Data Management 
  • Analysis Methods and Visualization

Data are the cornerstone of most practical machine learning applications. Getting acquainted with your data is thus one of the most (if not the most) important first steps when building AI systems. The main objective of this module is to give participants a good understanding of what “data” is, how to think statistically, as well as interpret basic statistical quantities and visualisations computed from data. We will also discuss what type of storage solutions facilitate the development of machine learning applications, and how data should be handled within an organization to leverage its full potential. Gaining “data literacy” is the main learning outcome of this module.

Learning outcomes

After completing this module, each participant will be able to

  • think statistically (based on data) about decision problems,
  • interpret basic aggregate statistics of data and understand their differences and pitfalls,
  • read and understand basic scientific visualisations of data,
  • explain differences between various data storage solutions from the perspective of machine learning,
  • explain the basic principles of how data is used in machine learning.

AI Methods

Modern tools within artificial intelligence (AI) are based on applying particular statistical models to massive datasets. In this module, we will look into the theory and application of some of the most popular methods used in AI. Covered topics include:

  • What is Machine Learning?
  • Supervised, unsupervised and reinforcement learning
  • Neural Networks
  • Model based Machine Learning

Learning outcomes

After completing this module, you know how to

  • identify and differentiate between different AI methods and machine learning types
  • choose the right method for your particular problem
  • critically evaluate the results delivered by AI methods
  • formulate a particular business case as a machine learning problem

AI Applications

In this module, we cover various applications of AI, driven by questions like the following. How does face recognition work? Can we write a program to automatically detect the sentiments expressed in text - or the objects shown in an image? How does Netflix decide what movies to recommend to you? They say “show me your friends, I’ll tell you who you are” - how much can you tell about someone from their friends and how?
 * We start by discussing different ways to represent text documents. * We then learn how to to detect sentiment in text using deep-learning methods, and generally how to categorize pieces of text using classification techniques. * We cover image classification tasks using convolutional neural networks. * We take a look at different types of recommender systems and various machine learning approaches to these. * Finally, we discuss algorithms for social network analysis - with an emphasis on machine learning algorithms that allow us to extract insights that are ‘hidden’ in the structure of networks.

Elective Module: AI in Business

  • AI business pipeline
  • Various case lectures exploring AI's opportunities and challenges
  • Platform business model and AI's role in it

Various AI methods and technologies open up vast opportunities and have generated a lot of hype recently. However, getting measurable business results in another story. The main objective of this module is to look at AI and ML from business perspective and explore their business opportunities as well as challenges. On top of that, you will learn fundamentals for platform economy and how AI & ML fit with it.

After completing this module, each participant will be able to

  • design AI projects from business need perspective
  • recognize opportunities and limitations of AI's use in business
  • understand platform business model

Elective Module: AI Programming

In this module, we focus into the actual programming side of AI. The essential motif of the module is to comprehend that AI programming can be understood and learned. AI still has needlessly a mystical aura around it, which we aim to unveil. Python programming language is used in the module. As a multi-purpose coding language it is suitable for applications such as machine learning, which forms the basis of artificial intelligence. Machine learning uses regression and data clustering to create and evaluate predictions. Modern tools within artificial intelligence are based on applying particular statistical models to massive datasets. We will look into the theory and application of some of the most popular methods used in AI.

Learning outcomes
 After completing this module, you know how to

  • Read and write different kinds of data sets in Python
  • Select the right tool and method in Python
  • Understand the principles behind AI solutions

AI in the Real World

In this module, we will consider the interaction of AI systems with the real world and society, as well as discuss issues encountered in continual development of AI systems that are in use. The module will close with an interactive discussion about ethics and sustainability in the context of AI with Antti Honkela (University of Helsinki), Kari Hiekkanen (Aalto University), Meeri Haataja (Saidot.AI) and Aleksi Rossi (Finnish Broadcasting Company).

Topics

  • Ethics: fairness, accountability, transparency
  • Privacy
  • Open data and open source
  • Continual development of AI systems
  • Learning outcomes



After completing this module, you will

  • understand possible ethical and privacy concerns in AI applications
  • know about the possibilities in using and contributing to open data and open source
  • understand challenges in continual development of production AI systems

Experts

Alex Jung

I'm interested in any aspect of machine learning for big data applications. Particular focus is given on sparse models (compressed sensing) and on the effects of constraints on computational complexity and communication requirements of the implemented learning algorithms. Recent work considers th...

Henri Schildt

Henri Schildt is a tenured professor with a joint appointment at the Aalto School of Business (Management & Organizations) and the School of Science’s Department of Industrial Engineering and Management. His research interests span digitalization, technology strategy, organizational change, a...

Marko Turpeinen

Peer-reviewed scientific articlesJournal article-refereed, Original researchFrom Space to Stage: How Interactive Screens Will Change Urban LifeKuikkaniemi, K.; Jacucci, G.; Turpeinen, M.; Hoggan, E.; Müller, J.2011 in ComputerISSN: 1128-5575The psychophysiology of James Bond: Phasic emotional res...

Teemu Roos

Teemu Roos is an expert and educator in AI, machine learning, and data science. His free Elements of Artificial Intelligence online course for non-experts is rated as the world's best computer science MOOC on Class Central. The course has more than 360 000 participants, and it is being translated...

Jaakko Hollmén

Hollmén is an expert in machine learning and data mining, especially their applications in bioinformatics and environmental time series analysis. Edited books Jaakko Hollmén, Panagiotis Papapetrou, editors. Proceedings of the ECMLPKDD 2015 Doctoral Consortium, Aalto University publication series...

Kari Hiekkanen

Kari Hiekkanen is 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. H...

Michael Mathioudakis

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 m...

Antti Ukkonen

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 ...

Laura Ruotsalainen

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 c...

Jussi Kangasharju

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/Ins...

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Diploma in Artificial Intelligence at Aalto University School of Business

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