Course Title

Robotics and Artificial Intelligence

ECTS Credits

6

Teaching Language

English

Instructor(s), Affiliation

To be confirmed

Delivery Method

Lectures

Tutorials

Independent Study Hours

Total

Hours

35

10

105

150

Pre-requisites or Other Academic Requirements

Programming:

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.

SYLLABUS

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

Assessment

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

Grading System

Letter Grading