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Machine learning

Individual course

Course contents

  • Components of machine learning: Data, hypothesis space and loss functions
  • Algorithms for machine learning: gradient descent, greedy search, linear solvers

Learning outcomes

After completing the course, the students

  • can formalise applications as ML problems and solve them using basic ML methods.
  • understand the concept of generalisation and how to analyse it using simple probabilistic models.
  • are familiar with linear models for regression and classification.
  • know how basic ML methods are obtained as combinations of particular choices for data representation (features), hypothesis space (model) and loss function.
  • are familiar with the idea of hard and soft clustering methods.
  • understand the basic idea of feature learning methods.

Teaching methods

The course follows a schedule and includes lectures, self study, assignments, and a project work.

The lectures might be organised on campus but will in any case be available online.

Workload

5 credits, approx. 130 hours of work divided into:

  • lectures + self-study (30 hours)
  • assignments (6 * 10 = 60 hours)
  • project work (around 40 hours)

For Aalto degree students: This course overlaps with Machine Learning: Basic Principles (CS-E3210) and Machine Learning with Python (CS-EJ3211) and only one of them can be included in the degrees. If you have already taken one of the basic machine learning courses, you should take the course Machine Learning: supervised methods (CS-E4710) instead.

More information in the Aalto University study guide.

You can get a digital badge after completing this course.

Further information about the courses and studying

Aalto University
FITech ICT contact person

Responsible teachers

Aalto University
Alex Jung, Assistant professor
Aalto University
Stephan Sigg, Associate professor

Contact person for applications

FITech Network University
Monica Sandberg, Student services specialist
Start here
Start here
Category:
ICT Studies
Topic:
AI and machine learning
Course code:
CS-C3240
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
10.1.–8.4.2022
Application deadline:
Application period has ended
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Online
Language:
English
General prerequisites:
Matrix algebra, probability theory, basic programming skills
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