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: 04.09.2023 - 29.11.2023
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
Contact teaching 56 h
Independent study 74 h
DETAILS
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 : 2022-2023 Autumn I - II
2023-2024 Autumn I - IIEnrollment :
Registration for Courses on Sisu (sisu.aalto.fi).