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Machine learning

Individual course

Course contents

  • Exploratory data analysis
  • Dimensionality reduction
  • PCA
  • Regression and classification
  • Clustering
  • Deep learning
  • Reinforcement learning

Learning outcomes

After completing the course, the students

  • can formalise applications as ML problems and solve them using basic ML methods
  • can perform basic exploratory data analysis
  • understand the meaning of the train-validate-test approach in machine learning
  • can apply standard regression and classification models on a given data set
  • can apply simple clustering and dimensionality reduction techniques on a given data set
  • are familiar with and can explain the basic concepts of reinforcement learning.

Teaching methods

The course follows a schedule and includes lectures, self study, assignments, and a project work. The lectures are available online.

Teaching times on campus:


  • Wednesdays at 14:15–16
  • Fridays at 12:15–14


  • Mondays at 8:15–10
  • Tuesdays at 16:15–18
  • Wednesdays at 8:15–10
  • Thursdays at 14:15–18
  • Fridays at 14:15–16


5 credits approx. 134 hours of work divided into:

  • Lectures + self-study: 10*(2+3) = 50 hours
  • Assignments: 6 * 9 = 54 hours
  • Project work: 26 hours
  • Peer-grading: 4 hours

More information in the Aalto University study guide.

You can get a digital badge after completing this course.

machine learning koneoppiminen ML data analyysi luokittelu regressio klusterointi

Responsible teachers

Aalto University
Pekka Marttinen, Associate professor
Aalto University
Stephan Sigg, Associate professor

Further information about the courses and studying

Aalto University
Tiina Porthén

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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AI and machine learning
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0 €
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Host university:
Aalto University
Who can apply:
Adult learner,
Degree student
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General prerequisites:
Matrix algebra, probability theory, basic programming skills.
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