FYS-STK4155 ¨C Applied Data Analysis and Machine Learning

Course content

The course introduces a variety of central algorithms and methods essential for studies of statistical data analysis and machine learning. The course is project-based and through the various projects, the students are?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 how to handle large scientific projects. 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 statistical data analysis and machine learning, with an emphasis on supervised learning.
• have knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers, data optimization, and their applications.
• understand linear and logistic regression methods.
• understand central optimization algorithms like stochastic gradient descent methods.
• have knowledge about central deep learning methods for both supervised and unsupervised learning.
• have experience working on numerical projects.
• have knowledge of other machine learning algorithms,?such as?decision trees, support vector machines,? ensemble methods like random forests, bagging, and boosting.

Admission to the course

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.

The course may be adapted for students who are unable to follow regular teaching on campus.

Recommended previous knowledge

Basic knowledge in programming and numerics:

• MAT1100 ¨C Calculus
• MAT1110 ¨C Calculus and Linear Algebra
• MAT1120 ¨C Linear Algebra

One, or more, of the following courses:

• INF1100 ¨C Introduction to programming with scientific applications (continued)
• IN1900 ¨C Introduction to Programming with Scientific Applications
• MAT-IN1105 ¨C Programming, Modelling and Computations (discontinued)
• IN-KJM1900 ¨C Introduction to Programming for Chemists
• BIOS1100 ¨C Introduction to computational models for Biosciences

Overlapping courses

• 10 credits overlap with FYS-STK3155 ¨C Applied Data Analysis and Machine Learning.

Teaching

The teaching is given throughout the semester:?

• 4 hours of lectures per week

• 2 hours of group sessions for work on computational projects per week, for about 15 weeks.

• Weekly assignments

Examination

• Home exams in the form of three project assignments that each counts?1/3 towards the?final grade.

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: FYS-STK3155 ¨C 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

In this course, postponed exams are not offered for exam candidates who are ill before the exam or who become ill during the exam. A deferred submission deadline can be offered.

The illness must be documented with a doctor's certificate dated no later than the ordinary submission date. You must submit the doctor's certificate to the course's contact point before the submission deadline for the home exam.

New exams are not offered to candidates who withdraw or do not pass the regular 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.