Lifewide learning courses and programmes

Machine learning

The course addresses components of machine learning such as data, hypothesis space and loss functions and it also introduces algorithms for machine learning: gradient descent, greedy search and linear solvers.


Teaching time:



Information and communications technology

Form of learning:

Online On-campus


Aalto University, FITech




5 By Aalto University (ECTS)


€ 0.00

Application period:

1.6.2023 – 29.8.2023

Target group and prerequisites

Matrix algebra, probability theory, basic programming skills.

Course description

Course contents

  • Exploratory data analysis
  • Dimensionality reduction
  • PCA
  • Regression and classification
  • Clustering
  • Deep learning
  • Reinforcement learning

Learning outcomes

After completing the course, the students

  • can formalise applications as ML problems and solve them using basic ML methods
  • can perform basic exploratory data analysis
  • understand the meaning of the train-validate-test approach in machine learning
  • can apply standard regression and classification models on a given data set
  • can apply simple clustering and dimensionality reduction techniques on a given data set
  • are familiar with and can explain the basic concepts of reinforcement learning.

Teaching methods

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


5 credits approx. 134 hours of work divided into:

  • Lectures + self-study: 10*(2+3) = 50 hours
  • Assignments: 6 * 9 = 54 hours
  • Project work: 26 hours
  • Peer-grading: 4 hours
  • Updated:
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