Machine learning

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.

Teaching times on campus:

Lectures:

• Wednesdays at 14:15–16
• Fridays at 12:15–14

Exercises:

• 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

Workload

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