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Machine learning: Supervised methods

Individual course

Max amount of FITech students: 40

Persons without a valid study right to a Finnish university 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.

Content:

  • 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.

More information on Aalto University’s WebOodi course page.

You can get a digital badge after completing this course.

koneaoppiminen tekoäly AI lineaarimallit algoritmit luokittelu optimointi

Further information about the studies

Aalto University
FITech ICT contact person
fitech-sci(at)aalto.fi

Contact person for applications

FITech
Pilvi Lempiäinen , Head of study services
pilvi.lempiainen(at)fitech.io
Start here
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Category:
ICT Studies
Topic:
AI and machine learning
Course code:
CS-E4710
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
7.9.–11.12.2020
Application deadline:
31.8.2020
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Blended
Place of contact learning:
Espoo
Language:
English
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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