PENG9590 Advanced Topics in Robotics and Control Course description

The course is structured in five modules:

• Module 1: Unsupervised Data Mining
• Module 2: Supervised Machine Learning
• Module 3: Reinforcement Learning
• Module 4: Artificial Neural Network and Deep Learning
• Module 5: Major Concepts in Artificial Intelligence, including: complex systems (networks, cellular automata, and agent-based models) and evolutionary computing

None.

Students who complete the course are expected to have the following learning outcomes, defined as knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student:

• has in-depth knowledge within specific topics in robotics and control that supplement the specialisation syllabus.
• is at the forefront of knowledge within the topic of his/her doctoral thesis project.
• has a profound understanding of the state-of-the-art and the latest developments in the field relevant to his/her doctoral thesis.

Skills

On successful completion of the course, the student can:

• apply theoretical knowledge, scientific methods and simulation tools suitable for solving complex robotics and control problems.
• plan and conduct scholarly work within the topic of his/her the doctoral thesis project.
• analyse existing theories, methods and standardised solutions on practical and theoretical engineering problems.

General competence

On successful completion of the course, the student:

• is competent in literature study, self-study and research-based learning
• can apply his/her knowledge and skills to carrying out advanced tasks and projects.
• can communicate issues, analyses and solutions to both specialists and non-specialists.
• can assess the need for, and initiate innovation in his/her field of expertise.

The course is carried out by research-based learning and a major study based on individual work, and is supervised by one or more supervisors (internal/external).

This course covers two central areas of scientific research: the construction and justification of a research plan, and the subsequent analysis and interpretation of its implementation and of the resulting data.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

None.

The student is expected to have the following outcomes on completion of the course:

Knowledge:

On successful completion of the course, the student:

• has advanced knowledge of the research process.
• has advanced knowledge of data collection techniques relative to engineering sciences.
• can critically assess the usefulness of using qualitative, quantitative, and mixed methodologies in the engineering sciences.
• has a high-level command of qualitative and quantitative methods of analysis relative to his/her field of study.

Skills:

On successful completion of the course, the student can:

• construct a problem statement or research question and evaluate its soundness.
• create technically and scientifically sound research proposals.
• select a methodology to address a research problem.

General competence:

On successful completion of the course, the student can:

• distinguish and formulate research problems.
• develop and critically assess the components of a research proposal.
• critically reflect on the nature of research, scientific practice and knowledge

This course can feature lectures and practical work to provide both theoretical and hands-on content. The students will supplement the lectures and group work with their own reading.