DIMECC Machine Learning Academy

August 31, 2022

Machine Learning Academy is a Competence Development and Upskilling Training Program Tailored for Finnish Manufacturing Industry.

Machine Learning Academy (MLA) includes diverse learning modules from ML algorithms to ethics, and from designing and managing artificial intelligence (AI) projects to implementing AI in the company business.
Machine Learning Academy uses a combination of theory, discussion, practical exercises and examples of existing application and business cases to emphasize the application of the methods and concepts.
The goal of the training is to increase the participants’ understanding of how to utilize AI and machine learning in their company.
After the MLA training, participants will understand the fundamentals of machine learning, as well as the ability to recognize and manage development tasks that aim to benefit from machine learning.

Machine Learning Academy in a nutshell:


  1. Basic understanding of the principles, methods, abilities and limitations of AI and ML.
  2. Current knowledge of related tools, commercial platforms, and implementations.
  3. Ability to specify, design, and lead activities aiming at applying ML methods, algorithms, and technologies into your business operations
  4. An Initial plan on when, where, and how you can deploy ML methods and technologies in your business.


DIMECC Machine Learning Academy is aimed for professionals working in the industry.

The course is aimed for the following roles:

  • R&D supervisors managing AI/ML development projects.
  • R&D engineers participating in AI/ML development projects.
  • Employees, who specify, and source work and subcontracting related to AI/ML


Tuesday 11.10.2022 Module 1: AI Crash course Introduction to the field of artificial intelligence including history and future of AI, core technologies, applications and relevant business cases. Detection of ML problems. Introduction to course, a model for AI project model and development tools to be utilized during the course.
Thursday 27.10.2022 Module 2: Supervised Learning Introduction to supervised learning and related algorithms. Linear algebra review. Linear regression. Individual exercises, group exercises.
Thursday 3.11.2022 Module 3: Data & Ethics and legislation How raw data usually needs to be pre-processed to make it usable in ML applications. Feature engineering. Know ethics and transparency of machine learning. Understand potential unintended consequences when using ML.
Tuesday 15.11.2022 Module 4: Unsupervised learning and deep Learning

Introduction to unsupervised learning. Clustering, dimensionality reduction. Showcasing applications, concrete business cases and commercial application. Individual exercises and group exercises.
Neural networks principles and learning concepts. Individual exercises and group exercises. Use of hardware in ML.

Tuesday 22.11.2022 Module 5: How to do ML Projects? Implementation of ML project in the company. Learn new ways to design and implement ML projects. Concrete business cases and design tools. Co-creation between all needed competences in ML project. Group exercises.
Tuesday 13.12.2022 Module 6: MLA Academy Project work presentations (+AI Fair) Conclusions, implementation of ML project in the company, networking to AI community, guidelines for next step.



A major component of DIMECC Machine Learning Academy is a course project that allows participants to investigate how machine learning could be deployed in their organizations and what kind of opportunities, challenges, requirements are related to the deployment.

The aim is to analyse in-depth how machine learning could be embedded in company’s strategies and business. The concrete objectives are:

  • Identify potential application areas
  • Define business cases and prioritize initiatives
  • Define the first concrete projects/pilots

The course project consists of group work, mid-term reporting and final presentation.

Course projects are done in groups. The idea is that participants from same organization form one group. This will help to build comprehensive picture how artificial intelligence and machine learning could be deployed in organization’s business.

Example practical result example from earlier MLA courses:

“One concrete example was Ponsse’s field project, which focused on after-sales services, especially field maintenance of the harvesting equipment where Machine Learning was to be used to recognize the needed oil change interval. Hydraulic oil and filters are currently changed at fixed intervals, approximately every 1800 hours, and optimized change interval would mean remarkable savings.”





Risto Lehtinen

Program Manager

+358 50 555 3900


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