IN2160 ¨C Probabilistic Machine Learning

Course content

The course provides an in-depth study of the foundations of machine learning. A number of commonly used machine learning methods and principles for model selection are described within a unified?probabilistic and statistical?framework. Relevant principles from statistics, information theory, decision theory, and optimization are introduced along the way. You will gain a conscious understanding of the theoretical properties of the individual methods and the relationships between different learning algorithms.

Learning outcome

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;

Admission to the course

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: maskinl?ring og kunstig intelligens (bachelor)?will be prioritized.

Special admission requirements

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).

Formal prerequisite knowledge

IN1000 ¨C Introduction to Object-oriented Programming

Recommended previous knowledge

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

STK-IN1050 ¨C Statistics for computer scientists

MAT1080 ¨C Mathematical foundation of machine learning

IN1160 ¨C Introduksjon til maskinl?ring

Teaching

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.

Examination

4 hours?written digital exam. All mandatory assignments must be approved to be allowed to take the exam.

Examination support material

No examination support material is allowed.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about?the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are?offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

More about examinations at UiO

• Use of sources and citations
• How to use AI as a student
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.