FYS9429 ¨C Advanced machine learning and data analysis for the physical sciences

Course content

Advances in artificial intelligence/machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of research topics in modern science, leading to advances that will facilitate scientific discoveries and societal applications.

This course focuses on advanced machine learning and statistical learning methods applied to a broad variety of problems in the physical sciences and life science, from computational neuroscience to the analysis of high-energy physics experiments. Supervised and unsupervised learning methods are discussed, spanning from various deep learning methods to Bayesian modeling.

Learning outcome

After completing this course, you should:?

• be familiar with central deep learning methods and how to use them in actual research.
• be familiar with advanced regression algorithms.?
• understand how to simulate complex physical processes with many degrees of freedom.
• understand optimization techniques and their fundamental role in machine learning.?
• be familiar with Bayesian statistics and Bayesian Machine Learning.
• understand how to find correlations in data sets and quantify uncertainties.?
• understand how to use Gaussian processes in the analysis of physics problems.

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through?Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers¡¯ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must?apply for a position as a visiting student?within a given deadline.

Capacity: 20 students

Recommended previous knowledge

A good background in mathematics is needed.

Other recommended courses:

• FYS-STK4155 ¨C Applied Data Analysis and Machine Learning?
• IN5400 ¨C Machine Learning for Image Analysis (continued)

Overlapping courses

• 10 credits overlap with FYS5429 ¨C Advanced machine learning and data analysis for the physical sciences.

Teaching

• Two weekly lectures, 45 minutes each, and to projects which are to be graded.

Examination

• Two projects (max. 10 pags per project) which are evaluated. Each project counts 50% and you need to pass both projects in order to pass the course. The projects are to be delivered in Inspera.

?

When writing your exercises make sure to familiarize yourself with the rules for use of sources and citations. Breach of these rules may lead to suspicion of attempted cheating.

It will also be counted as one of the three attempts to sit the exam for this course if you sit the exam for one of the following courses: FYS5429 ¨C Advanced machine learning and data analysis for the physical sciences

Examination support material

All examination support material is allowed.

Grading scale

Grades are awarded on a pass/fail scale. 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 exam?at the beginning of the next semester.

New examinations?are offered at the beginning of the next semester for students who do not successfully complete the exam during the previous semester.

We do not offer a re-scheduled exam for students who withdraw during the exam.

More about examinations at UiO

• Use of sources and citations
• 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.