IN2160 – Probabilistic Machine Learning

After taking the course:

• You can explain how probability theory and uncertainty are fundamental to key methods in machine learning;
• You understand the core principles behind modern machine learning, including supervised and unsupervised learning techniques, and their mathematical foundations in statistical learning theory;
• You can implement and apply important algorithms such as linear and logistic regression, Gaussian processes, decision trees, and neural networks;
• You can formulate problems mathematically and choose appropriate models and algorithms for different learning tasks, considering model assumptions and inductive biases;
• You have a solid understanding of key concepts such as overfitting, regularization, model complexity, the bias-variance trade-off, and how probabilistic criteria can be used to evaluate and compare learning algorithms;
• You can explain the connections between different algorithmic approaches, such as generative vs. discriminative models, and how they fit into the broader landscape of machine learning;

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Students belonging to the Bachelor's programme Informatikk: maskinlæring og kunstig intelligens (bachelor) will be prioritized.

In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:

• Mathematics R1 or Mathematics (S1+S2)

The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies. Read more about special admission requirements (in Norwegian).

The course builds on basic knowledge in mathematics, statistics, and machine learning. It is highly recommended to have completed the following courses:

STK-IN1050 – Statistics for computer scientists

MAT1080 – Mathematical foundation of machine learning

IN1160 – Introduksjon til maskinlæring

4 hours of lectures and 2 hours of group teachings.

Submission of compulsory assignments is required. Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments.