Learning outcomes

In order to pass the course, a student must be able to

(1) apply basic principles of machine learning to natural language data,

(2) use standard software packages for machine learning,

(3) implement linear models for simple and structured classification,

(4) apply clustering techniques to natural language data,

with a certain degree of independent creativity, clearly stating and critically discussing methodological assumptions, applying state-of-the-art methods for evaluation, and presenting the result in a professionally adequate manner.

Instruction

The teaching is given as lectures and laboratory sessions under supervision.

Assessment

The course is examined by means of three assignments:

- Decision trees and nearest neighbour classification

- Perceptron learning

- Clustering

In order to pass the course, a student must pass each of one of these. In order to pass the course with distinction (Väl godkänt), a student must pass at least two assignments with distinction.