Supervised machine learning

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) on Tuesdays at 10:15-12:00.
• Exercise sessions (Otaniemi) on Fridays at 10:15-12:00.
• The exam (Otaniemi) on 9.12. 13-16.

Attendance in lectures and exercise sessions is voluntary, recordings from lectures available.

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.

This course was previosly under the name Machine learning: Supervised methods.

You can get a digital badge after completing this course.

koneaoppiminen tekoäly AI lineaarimallit algoritmit luokittelu optimointi