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Machine learning: Supervised methods

Individual course

Max amount of FITech students: 40

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Mastering the prerequisite skills is very important in order to complete this course. Please list your preliminary knowledge in your application.

Course contents

  • Generalization error analysis and estimation
  • Model selection
  • Optimization and computational complexity
  • Linear models
  • Support vector machines and kernel methods
  • Boosting
  • Feature selection and sparsity
  • Multilayer perceptrons
  • Multi-class classification
  • Ranking
  • Multi-output learning

After the course, the student

  • knows how to recognize and formalize supervised machine learning problems,
  • knows how to implement basic optimization algorithms for supervised learning problems,
  • knows how to evaluate the performance supervised machine learning models,
  • has understanding of the statistical and computational limits of supervised machine learning, as well as the principles behind commonly used machine learning models.

Completion methods

Lectures (online) will be held on Tuesdays at 10:15-12:00. Exercise sessions (in Otaniemi) will be held on Fridays at 10:15-12:00.

The exam will be held on 20.12.2021 at 17:00-20:00 in Otaniemi.

More information on Aalto University’s course page.

You can get a digital badge after completing this course.

koneaoppiminen tekoäly AI lineaarimallit algoritmit luokittelu optimointi

Responsible teacher

Aalto University
Juho Rousu

Further information about the studies

Aalto University
FITech ICT contact person

Contact person for applications

FITech Network University
Monica Sandberg, Student services specialist
Start here
Start here
ICT Studies
AI and machine learning
Course code:
0 €
Teaching period:
Application deadline:
Application period has ended
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Place of contact learning:
General prerequisites:
Courses CS-C3190 Machine Learning, MS-C1620 Statistical inference or equivalent knowledge. Basics of probability theory. Basic linear algebra. Programming skills.
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