FYS-STK3155 – Applied Data Analysis and Machine Learning

Course description

• Course content
• Learning outcome
• Admission
• Prerequisites

• Overlapping courses
• Teaching
• Examination
• Evaluation

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 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 experience from working on numerical Projects.

Admission

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.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

Prerequisites

Formal prerequisite knowledge

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:

• MAT1100 – Calculus
• MAT1110 – Calculus and Linear Algebra
• MAT1120 – Linear Algebra

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

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.
• Three compulsory projects, that each will account for 1/3 of your final grade.
• Weekly assignments.

Examination

• Three projects that each account for 1/3 of your final grade.

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.

Explanations and appeals

• Explanation of grades and appeals

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.