Machine Learning

Course manual 2025/2026

Course content

Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. One case where learning is necessary is when human expertise does not exist, or when humans are unable to explain their expertise.

Already, there are many successful applications of machine learning in various domains: There are commercially available systems for recognizing speech and handwriting. Retail companies analyze their past sales data to learn their customers’ behavior to improve customer relationship management. Financial institutions analyze past transactions to predict customers’ credit risks. Robots learn to optimize their behavior to complete a task using minimum resources. These are some of the applications that we will discuss in this lecture.

We can only imagine what future applications can be realized using machine learning: Cars that can drive themselves under different road and weather conditions, autonomous robots that can navigate in a new environment, for example, on the surface of another planet, as well as personal AI assistants that enhance our capabilities. Machine learning is certainly an exciting field to be working in!

Study materials

Other

• Course notes, recommended literature and slides on Canvas

Objectives

• The student can explain the primary goal of machine learning, the fundamental methods by which this is achieved, and the inherent limitations.
• The student can analyse a machine learning task and compare and contrast it in terms of e.g. unsupervised or supervised, regression or classification, etc.
• The student can apply foundational machine learning algorithms to discrete and/or continuous data and interpret the result.
• The student can analyse the fit of a learned model, diagnosing underfitting or overtfitting, hyperparameter sensitivity, generalization etc., then use these results to tune and improve the model.
• The student can evaluate a complete machine learning pipeline (including data collection, data processing, model application and validation), and choose an approach that can improve the result.

Teaching methods

• Lecture
• Seminar
• Laptop seminar
• Self-study

Learning activities

Attendance

Programme's requirements concerning attendance (TER-B Article B-4.10):

• For some course component attendance is obligatory. If attendance is required, this is stated in the course catalogue. The reasons for, and the implementation of, this attendance requirement may vary by course and are included in the course manual. Students who do not meet this attendance requirement cannot complete the course with a passing grade.
  

Additional requirements for this course:

Het wordt sterk aanbevolen om naar de laptop- en werkcolleges te gaan, maar er is geen aanwezigheidsplicht.

Assessment

There will be weekly programming assignments during the course. The course will conclude with one mid-term exam, one final exam and one retake exam.

Minimum required grade for the programming assignments is 5, if the student fails to obtain a minimum of 5 from the assignments (all programming assignments weighted together), the student will fail the course.
Minimum required grade for the exams is 5, if the student fails to obtain a minimum of 5 (mid-term and final weighted together), the student will fail the course.

The grade will be computed as follows:

Total = 0.35*mid-term + 0.45*final + 0.2*programming

If the student fails, the retake exam will affect the grade as follows:

Total = 0.8*retake + 0.2*programming

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

Additional information

Recommended prior knowledge: having successfully completed BSc level Linear Algebra, Calculus, Optimization, and Statistics courses is a requirement. Without basic math and statistics knowledge this course can not be successfully completed. 

Course is taught in English

Contact information

Coordinator

• Christian Andersson Naesseth