- Exploratory data analysis
- Dimensionality reduction
- Regression and classification
- Deep learning
- Reinforcement learning
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.
The course follows a schedule and includes lectures, self study, assignments, and a project work. The lectures are available online.
Teaching times on campus:
- Wednesdays at 14:15–16
- Fridays at 12:15–14
- Mondays at 8:15–10
- Tuesdays at 16:15–18
- Wednesdays at 8:15–10
- Thursdays at 14:15–18
- Fridays at 14:15–16
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
More information in the Aalto University study guide.
You can get a digital badge after completing this course.
machine learning koneoppiminen ML data analyysi luokittelu regressio klusterointi
Lisätietoa kursseista ja niiden suorittamisesta
Hakua koskevat kysymykset