STK9030 – Statistical Learning: Advanced Regression and Classification
Course description
Course content
Modern data analysis refer to methods where fewer assumptions (such as a linear relation between response and explanatory variables) are made and where instead data determine the relation. Some keywords are nearest neighbor methods, kernel smoothing and generalized additive models. Statistical classification is problems where the response variable is a categorical variable("classes"). The course will present classical classification methods as well as more advanced methods based on modern regression methods. A central problem in the course is searching for structures in data, often referred to as "data mining" or "learning from data".
Learning outcome
During the course you will learn many different methods for regression and classification. You will learn practical use of these methods as well as basic understanding of them. You will also learn how to choose between the different available methods.
Admission
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
Prerequisites
Recommended previous knowledge
STK1100 – Probability and Statistical Modelling, STK1110 – Statistical Methods and Data Analysis, STK2120 – Statistical Methods and Data Analysis 2 (discontinued).
Overlapping courses
- 10 credits overlap with STK4030 – Statistical Learning: Advanced Regression and Classification (discontinued)
- 5 credits overlap with UNIK4590 – Pattern Recognition (continued)
- 3 credits overlap with INF-STK5010 – Statistical Bioinformatics - Learning from big data in the life sciences (discontinued)
- 3 credits overlap with INF-STK9010 – Statistical Bioinformatics - Learning from big data in the life sciences (discontinued)
- 2 credits overlap with INF9540
The information about overlaps is not complete. Contact the department for more information if necessary.
Teaching
3 hours of lectures/exercises per week.
Examination
Depending on the number of students, the exam will be in one of the following four forms:
1.Only written exam
2.Only oral exam
3.A project paper followed by a written exam.
4.A project paper followed by an oral exam/hearing.
For the latter two the project paper and the exam counts equally and the final grade is based on a general impression after the final exam. (The two parts of the exam will not be individually graded.)
What form the exam will take will be announced by the teaching staff within October 15th for the autumn semester and March 15th for the spring semester.
In addition, each phd student is expected to give a one hour oral presentation on a topic of relevance (chosen in cooperation with the lecturer). The presentation has to be approved by the lecturer for the student to be admitted to the final exam.
Examination support material
Permitted aids at the exam if written: Approved calculator.
Oral exam: No aids permitted.
Information about approved calculators (Norwegian only)
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 pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.