Credits: 5
Schedule: 09.09.2019 - 04.12.2019
Teaching Period (valid 01.08.2018-31.07.2020):
I-II (autumn) 2018 - 2019
I-II (autumn) 2019 - 2020
Learning Outcomes (valid 01.08.2018-31.07.2020):
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.
Content (valid 01.08.2018-31.07.2020):
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 01.08.2018-31.07.2020):
Assignments and project work.
Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation):
Grading 0-5. Quizzes 20 %, Assignments 50 %, Project 30 %. No exam.
To pass: Completed assignments. Completed project.
Workload (valid 01.08.2018-31.07.2020):
Contact teaching, independent study, assignments, project
Contact teaching 56 h
Independent study 74 h
Study Material (valid 01.08.2018-31.07.2020):
Lecture notes. On-line material.
Prerequisites (valid 01.08.2018-31.07.2020):
Required: Basic programming skills, basic calculus (gradient), basic vector and matrix algebra, basic probability (random variables, expectation)
Recommended: Artificial Intelligence
Useful: Machine learning - basic principles, Digital and optimal control, Stochastics and estimation
Grading Scale (valid 01.08.2018-31.07.2020):
0-5
Further Information (valid 01.08.2018-31.07.2020):
Language class 3: English
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