Courses

One of our goals is to prepare future workforce for the up-and-coming autonomous driving industry in Estonia (and elsewhere) — this is a long process. Providing the students here at the Institute of Computer Science with a variety of courses that provide them with an opportunity to get familiar with the basics of self-driving software, is a critical first step.

Here is an overview of the different courses taught at our institute. Be sure to check out the links that take you to the Study Information System, if you’re interested in the specifics!

INTRODUCTORY COURSE

Fundamentals of Autonomous Driving

6 ECTS
LTAT.06.011
Lecturer: NAVEED MUHAMMAD
Content and relation to ADL activities:
Introduces all the sub-problems that need to be solved to build an autonomous driving system.
INTRODUCTORY COURSE

Introduction to Data Science

6 ECTS
LTAT.02.002
Lecturer: MEELIS KULL
Content and relation to ADL activities:
Sensor data analysis is at the core of modern self-driving. This course gives a brief overview of the basic concepts, principles and practice of data science. The main goal is to learn to plan and carry out a simple practical data science project.
RELATED SUBTASKS

Machine Learning

6 ECTS
MTAT.03.227
Lecturer: DMYTRO FISHMAN
Content and relation to ADL activities:
Introduces the core concepts of machine learning and data science. Allows to do a project related to AD.
RELATED SUBTASKS

Neural Networks

6 ECTS
LTAT.02.001
Lecturer: RAUL VICENTE
Content and relation to ADL activities:
Introduces ML algorithms called neural networks (NNs). NNs are mainly used in the perception module of modular stacks for autonomous driving, but also in other modules. In end-to-end driving approaches, the entire driving stack is one big neural network. Also, there is a possibility to do a project that is related to autonomous driving.
RELATED SUBTASKS

Deep Learning for Computer Vision

6 ECTS
LTAT.02.028
Lecturer: Dmytro Fishman
From recognising number plates to detecting highly invasive renal masses, computer vision algorithms have become a key driving force behind many areas vital for modern society, including transportation, medicine, e-commerce, art, and entertainment. Deep neural networks are at the heart of this progress. This course will dive into the core computer vision tasks such as image classification, object detection, and segmentation from the perspective of deep neural networks. We will review the main architectures, training pipelines, and performance metrics used in these tasks. Additionally, we will explore more advanced computer vision topics, such as image generation, attention and transformers, and self-supervised and weakly supervised learning. Students will gain the practical skills necessary to complete a real-life computer vision project.
RELATED SUBTASKS

Optimization for Robot Control

3 ECTS
LOTI.05.084
Lecturer: ARUN SINGH
Content and relation to ADL activities:
Basics on optimization, especially in light of trajectory optimization, model predictive control from the point of view of robotics motion planning and control.
RELATED SUBTASKS

Motion Planning and State Estimation in Robotics

3 ECTS
LOTI.05.083
Lecturer: ARUN SINGH
Content and relation to ADL activities:
Basics of robot motion planning, control, and state estimation. Tasks like navigating mobile robots through obstacle filled environments.
RELATED SUBTASKS

Autonomous Vehicles Project

6 ECTS
LTAT.06.012
Lecturer: NAVEED MUHAMMAD
Content and relation to ADL activities:
Allows you to spend a considerable amount of time on a topic of your choice related to AD.
RELATED SUBTASKS

Robotics Technology

6 ECTS
LOTI.05.057
Lecturer: KARL KRUUSAMÄE
Content and relation to ADL activities:
Introduces using the Robot Operating System (ROS) that is also used on the ADL’s self-driving Lexus.
RELATED SUBTASKS

Robotics II

12 ECTS
LOTI.05.088
Lecturer: JAANO JÕGEVA
Content and relation to ADL activities:
Introduces mapping (SLAM) and computer vision related topics, alongside hands-on robotics
RELATED SUBTASKS

Data Science Project

6 ECTS
LTAT.00.009
Lecturer: SVEN LAUR
Content and relation to ADL activities:
There is a possibility to do a project related to AD.
RELATED SUBTASKS

High Performance Computing (HPC)

3 ECTS
LTAT.06.026
Lecturer: IVAR KOPPEL
Content and relation to ADL activities:
The use of university high performance cluster for training machine learning models and running simulations.
RELATED FIELDS

QGIS with Fundamental of GIS

3 ECTS
LTOM.02.034
Lecturer: KIIRA MÕISJA
Content and relation to ADL activities:
Introduces basics of geoinformatics via QGIS freeware. Amongst other things, QGIS is also relevant for creating maps used by self-driving cars.
RELATED FIELDS

Spatial Databases

6 ECTS
LTOM.02.040
Lecturer: VALENTINA SAGRIS
Content and relation to ADL activities:
Introduces how spatial data can be represented and treated. Spatial data in the form of maps is crucial for modular approaches in self-driving.
RELATED FIELDS

Intelligent Transportation Systems

6 ECTS
MTAT.08.040
Lecturer: AMNIR HADACHI
Content and relation to ADL activities:
Topics related to autonomous transportation, such as intelligent traffic management and intelligent infrastructure.