FYS-STK3155 – Applied Data Analysis and Machine Learning
Course description
Changes in the course due to coronavirus
Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.
See general guidelines for examination at the MN Faculty autumn 2020.
Course content
The course introduces a variety of central algorithms and methods essential for studies of data analysis and machine learning. The course is project based and through the various projects, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course.
Learning outcome
After completing the course you will:
- Have a basic understanding of Bayesian statistics and learning and common distributions
- Have an understanding of central algorithms used in data analysis and machine learning
- Have knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers, data optimization, and their possible applications, from numerical integration to simulation of stock markets
- Understand linear methods for regression and classification
- Have knowledge about neural network, genetic algorithms and Boltzmann machines
- Have experience from working on numerical projects.
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.
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 and S2) + R2
And in addition one of these:
- Physics (1+2)
- Chemistry (1+2)
- Biology (1+2)
- Information technology (1+2)
- Geosciences (1+2)
- Technology and theories of research (1+2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).
Recommended previous knowledge
Basic knowledge in programming and numerics:
One, or more, of the following courses:
- INF1100 – Introduction to programming with scientific applications (continued)
- IN1900 – Introduction to Programming with Scientific Applications
- MAT-INF1100 – Modelling and Computations
- MAT-INF1100L – Programming, Modelling and Computations (continued)
- MAT-IN1105 – Programming, Modelling and Computations
- IN-KJM1900 – Introduksjon i programmering for kjemikere
- BIOS1100 – Introduction to computational models for Biosciences
Overlapping courses
- 10 credits overlap with FYS-STK4155 – Applied Data Analysis and Machine Learning.
- 3 credits overlap with IN3050 – Introduction to Artificial Intelligence and Machine Learning.
- 3 credits overlap with IN4050 – Introduction to Artificial Intelligence and Machine Learning.
Teaching
- Four lectures per week, for about 15 weeks.
- Four hours of laboratory sessions for work on computational projects per week, for about 15 weeks.
- Weekly assignments.
Examination
- Three mandatory project assignments that each counts 1/3 towards the final grade.
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: FYS-STK4155 – Applied Data Analysis and Machine Learning
Language of examination
Subjects taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.
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
This course offers both postponed and resit of examination. Read more: