Machine Learning and Language Models

Course manual 2025/2026

Course content

This unit will provide an overview of established tasks and algorithms in machine learning and how these are applied to problems in language modelling. Students will learn how to use key Python packages for machine learning, as well as having the opportunity to implement algorithms for themselves. The course will consist of lectures, in which key concepts will be taught, and lab sessions, in which students will complete programming worksheets. The first half of the course will cover various supervised learning algorithms, including a focus on neural network architectures; unsupervised learning, including clustering and dimensionality reduction; and concepts in reinforcement learning. The second half of the course will give an introduction to key concepts and tasks in language modelling, and how machine learning is used to perform these tasks.

Objectives

• Explain basic concepts and assumptions underlying key machine learning algorithms.
• Implement key machine learning algorithms using Python and relevant packages.
• Rigorously compare the performance of competing methods.
• Explain key concepts, problems, and tasks in language modelling.
• Use small-scale language models for natural language processing tasks.

Teaching methods

• Lecture
• Seminar
• Self-study

Learning activities

Attendance

This programme does not have requirements concerning attendance (TER-B).

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• Martha Lewis