IN-STK9100 – Reinforcement Learning and Decision Making Under Uncertainty
Course description
Course content
This course gives a firm foundation to reinforcement learning and decision theory from mainly a statistical, perspective. The aim of the course is two-fold. Firstly, to give a thorough understanding of statistical decision making, Markov decision processes, and the relation of statistical decision making to human decision making. Secondly, to relate the theory to practical problems in reinforcement learning and artificial intelligence through algorithm design, implementation and a group project in reinforcement learning.
Learning outcome
After taking the course, you will:
- Understand the principles of decision theory.
- Understand the basics of Bayesian inference
- Understand Markov Decision Processes
- Understand Dynamic Programming
- Be able to design and implement Reinforcement Learning algorithms
- Be able to critically read research papers in reinforcement learning?
- Be able to perform reinforcement learning research
Admission to the course
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.
PhD candidates who have been admitted to another higher education institution must?apply for a position as a visiting student?within a given deadline.
The course is limited to 30 students (IN-STK5100 and IN-STK9100 together). If the number of enrolled students is higher than the limit, they will be ranked as follows:
- PhD candidates who have the topic approved in their study plan
- Master?s students at the master?s program?Data Science?who has the course approved in their study plan
- Master?s students at the Faculty of Mathematics and Natural Sciences who have?the course?approved in their study plan
- Master?s students at the Faculty of Mathematics and Natural Sciences
- Other
Recommended previous knowledge
This is a challenging course, so it is highly recommended that you know at least?
- Elementary Python programming skills (IN1000, IN1900 or equivalent experience)
- Basic linear algebra and calculus (MAT1100/1120, MAT1110 or equivalent)
- Elementary probability and statistics (STK1000, STK1100)
- A more advanced course like IN-STK5000 is advantageous
Overlapping courses
- 10 credits overlap with IN-STK5100 – Reinforcement Learning and Decision Making Under Uncertainty (discontinued).
Teaching
The course will consist of
- 4 hours of lectures/lab per week, for the first part of the semester
- Then 2 hours of lab (project work) per week, for the last part of the semester
Completion of mandatory assignments is required.?Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments.
Examination
All mandatory assignments must be approved before you can take the final digital exam.
Candidates will be assessed based on:
Group Project
Written Digital Exam
All parts must be passed, and they must be passed in the same semester.
It will also be counted as one of?your three?attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN-STK5100 – Reinforcement Learning and Decision Making Under Uncertainty (discontinued)
Examination support material
Any written material + Calculator.
Grading scale
Grades are awarded on a pass/fail scale. Read more about?the grading system.
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.