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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

  • Generalisation error analysis and estimation
  • Model selection
  • Optimisation and computational complexity
  • Linear models
  • Support vector machines and kernel methods
  • Boosting
  • Feature selection and sparsity
  • Multi-layer perceptrons
  • Multi-class classification
  • Preference learning

Learning outcomes

After the course, the student

  • knows how to recognise and formalise supervised machine learning problems,
  • knows how to implement basic optimisation 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.

Course material

Supplementary reading:

  • Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning
  • Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press

Teaching schedule

Lectures (Otaniemi) will be held on Tuesdays at 10:15-12:00. Exercise sessions (in Otaniemi) will be held on Fridays at 10:15-12:00. Attendance in lectures and exercise sessions is voluntary, recordings from lectures available. The exam is in Otaniemi on 11.12. 17-20.

Completion methods


  • 24 lecture hours
  • 12 hours exercise session
  • 3 hours exam
  • 96 hours independent study

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
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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Host university:
Aalto University
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Adult learner,
Degree student
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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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