FYS-STK4155 – Applied data analysis and machine learning
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.
After this course you will :
- Have a basic knowledge 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 knowledge of other machine learning algorithms like decision trees, support vector machines and nearest neighbors;
- Have experience from working on numerical Projects.
Students admitted at UiO must apply for courses in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
Nordic citizens and applicants residing in the Nordic countries may apply to take this course as a single course student.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.
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 Modelling in the Biosciences
10 credits overlap with FYS-STK3155 – Applied data analysis and machine learning
- Four lectures per week, for about 15 weeks.
- Four hours of laboratory sessions for work on computational projects per week, for about 15 weeks.
- Three compulsory projects, that each will account for 1/3 of your final grade.
- Weekly assignments.
- Three projects that each account for 1/3 of your final grade.
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.
Explanations and appeals
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.