How to make (almost) anything

This course is the first course of the Robotics specialisation.

The course will give an overview of the most important manufacturing methods like 3D printing, NC milling, laser cutting or moulding. In addition, we will explain how to design simple electric circuits to handle sensors and actuators and how to design printed circuit boards. These techniques will allow students to design physical prototypes on their own at the end of the course.

Intended learning outcomes
After the course, the student should be able to:

  • analyse and compare the main manufacturing methods
  • model 3D parts and assemblies using Computer Aided Design (CAD) Software at beginner level
  • select the best manufacturing process for a component
  • design simple electronic systems and circuit boards
  • Use different manufacturing techniques on custom-designed parts
  • Design complete, but simple mechatronic systems and prototype small mechanisms

Link:How to make (almost) anything

Advanced Robotics

This course is the last course in the robot specialization. The course provides an introduction to robotics and starts by answering general questions such as what a robot is, their origins, types, and applications. It then introduces the key approaches to robot control and explores the pros and cons of each. The course finally covers the advanced topics of robot learning and bio-inspired robots in addition to current robot research topics at ITU. In addition to the theory, the course also has a significant practical, hands-on dimension that comprises introduction to relevant software tools (simulators and operating systems) and hands-on experiments with simulated and physical robots.

Intended learning outcomes
After the course, the student should be able to:

  • Describe what a robot is, their origin, types, and applications
  • Describe, compare, and apply robot control stategies
  • Apply robot software tools (simulation, operating systems)
  • Apply bio-inspired solutions and machine learning appropriately in robotics
  • Formulate and reflect on robot solutions to real-world problems

Link:Advanced Robotics

Modern Artificial Intelligence

Modern artificial intelligence and computational intelligence have many applications inside and outside computer games. The techniques taught in this course are applicable to games, simulation environments, robotics, and many other areas.
Students learn a broad understanding of the theoretical, practical and implementation side of AI algorithms.

Intended learning outcomes
After the course, the student should be able to:

  • Theorize about and describe the AI algorithms covered in the class.
  • Identify tasks that can be tackled through advanced AI techniques and select the appropriate technique for the problem under investigation.
  • Compare the performance of different AI techniques and reflect on their suitability for different domains
  • Design and implement efficient and robust advanced AI algorithms.
  • Work efficiently in groups

Link:Modern Artificial Intelligence