LEARNING OUTCOMES
After completing the course, a student can: (I) explain main concepts and approaches related to decision making and learning in stochastic time series systems; (ii) read scientific literature to follow the developing field; (iii) implement algorithms such as value iteration and policy gradient.
Credits: 5
Schedule: 02.09.2024 - 27.11.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Joni Pajarinen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Modeling uncertainty. Markov decision processes. Model-based reinforcement learning. Model-free reinforcement learning. Function approximation. Policy gradient. Partially observable Markov decision processes.
Assessment Methods and Criteria
valid for whole curriculum period:
Assignments and project work.
Workload
valid for whole curriculum period:
Contact teaching, independent study, assignments, project
DETAILS
Study Material
valid for whole curriculum period:
Lecture notes. On-line material.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - II