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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
  • Multi-layer perceptrons
  • Multi-class classification
  • Preference learning

Learning outcomes

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.

Course material

Supplementary reading:

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

Teaching schedule

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. Attendance in lectures and exercise sessions is voluntary.

The exam will be held on 12.12.2022 at 17:00-20:00 in Otaniemi.

Completion methods

Workload:

  • 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
FITech ICT contact person

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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Category:
ICT Studies
Topic:
AI and machine learning
Course code:
CS-E4710
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
6.9.–12.12.2022
Application deadline:
29.8.2022
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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