Course Description: In this course we will study the latest methods in Machine Learning as they apply to the field of robotics. In particular we will study: Reinforcement learning, Gaussian Processes, Deep Learning, and Deep Reinforcement Learning. The course will be divided into studying background material and state of the art papers.
Course learning outcomes:
- Develop necessary research skills
- Conduct a literature review on a selected topic
- Present a scientific paper
- Summarize the content of a presentation
- Develop the ability to work with robotic systems
- Develop the ability to work with learning frameworks
Prerequisites:
There are no prerequisite courses. However, students are expected to have strong software development skills. Projects are likely to involve some combination of C++, Python, bash, Linux, ROS, LATEX, and other tools as needed. Experience in machine learning and robotics is recommended, but strong students may be able to acquire the necessary background on-the-fly.
Instructor
Evaluation:
- Assigniments (5) 10% each: 50%
- Presentation Summary 10%
- Class participation 10%
- Presentations 30%
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