The course information as follows may be subject to change, either during the session because of unforeseen circumstances, or following review of the course at the end of the session. Questions about the course should be directed to the course instructor.


Course Title

Robotics and Artificial Intelligence

ECTS Credits


Teaching Language


Instructor(s), Affiliation

To be confirmed

Delivery Method



Independent Study Hours







Pre-requisites or Other Academic Requirements


Familiarity with the basics of Python

Python 3.7 or 3.8 installed as a part of the Anaconda Python distribution of Data Science, or equivalent.

Libraries: scipy, numpy, matplotlib, pandas, sklearn

Mathematics and statistics:

Working knowledge of linear algebra, calculus, basic probability and statistics.



Course Overview

This course provides an introductory overview of the field of robotics. Topics related to how robots move, perceive and interact will be discussed. The first half of the course will cover fundamental concepts in robot kinematics, computer vision, robot control, localization and planning. In the second half, the course will cover the foundations of machine learning and AI and explain how a neural network is built and trained. Each topic is accompanied by a programming tutorial where the students will learn to implement the concepts in a hands-on way.

Learning Outcomes

By the end of the course, the student should learn

       - The theory behind the various components of a robot

       - To describe the motion of a robot, how it perceives and interacts with its surroundings

       - To use machine learning to fit models to data

       - To build neural networks to extract patterns from complex data and make predictions

       - To program all of this in Python

Textbook and Supplementary Readings

"Introduction to Autonomous Mobile Robots". R. Siegwart, I. Nourbakhsh, D. Scaramuzza.

"Artificial Intelligence – A Modern Approach (3rd Edition)". S. Russel, P. Norvig.

"Make your own Neural Network". T. Rashid


The final assignment is a programming project done in groups of 4-5 students, 100% of the final grade.

Grading System

Letter Grading