FYS-STK3155 – Applied Data Analysis and Machine Learning

After completing the course you will:​

• have a basic understanding of common probability distribution functions.
• have an understanding of central algorithms used in statistical data analysis and machine learning, with an emphasis on supervised learning.
• 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.

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.

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

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).

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-IN1105 – Programming, Modelling and Computations (discontinued)
• IN-KJM1900 – Introduction to Programming for Chemists
• BIOS1100 – Introduction to computational models for Biosciences (discontinued)

The teaching is given throughout the semester:

• 4 hours of lectures per week
• 4 hours of group sessions for work on computational projects per week, for about 15 weeks.
• Weekly assignments

• Home exams in the form of three project assignments that each counts 1/3 towards the final grade. All parts of the exam must be submitted in order to receive a grade in the course.

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-STK4155 – Applied Data Analysis and Machine Learning

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.